# Multimodal Chain-of-Thought Reasoning in Language Models

###### Abstract

Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies have primarily focused on the language modality. We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework that separates rationale generation and answer inference. In this way, answer inference can leverage better generated rationales that are based on multimodal information. Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach. With Multimodal-CoT, our model under 1 billion parameters achieves state-of-the-art performance on the ScienceQA benchmark. Our analysis indicates that Multimodal-CoT offers the advantages of mitigating hallucination and enhancing convergence speed. Code is publicly available at https://github.com/amazon-science/mm-cot .

Reviewed on OpenReview: https://openreview.net/forum?id=y1pPWFVfvR

## 1 Introduction

Imagine reading a textbook with no figures or tables. Our ability to knowledge acquisition is greatly strengthened by jointly modeling diverse data modalities, such as vision, language, and audio. Recently, large language models (LLMs) ( Brown et al., 2020 ; Thoppilan et al., 2022 ; Rae et al., 2021 ; Chowdhery et al., 2022 ) have shown impressive performance in complex reasoning by generating intermediate reasoning steps before inferring the answer. The intriguing technique is called chain-of-thought (CoT) reasoning ( Wei et al., 2022b ; Kojima et al., 2022 ; Zhang et al., 2023d ) .

However, existing studies related to CoT reasoning are largely isolated in the language modality ( Wang et al., 2022c ; Zhou et al., 2022 ; Lu et al., 2022b ; Fu et al., 2022 ) , with little consideration of multimodal scenarios. To elicit CoT reasoning in multimodality, we advocate a Multimodal-CoT paradigm. Given the inputs in different modalities, Multimodal-CoT decomposes multi-step problems into intermediate reasoning steps (rationale) and then infers the answer. Since vision and language are the most popular modalities, we focus on those two modalities in this work. An example is shown in Figure 1 .

In general, Multimodal-CoT reasoning can be elicited through two primary paradigms: (i) prompting LLMs and (ii) fine-tuning smaller models. 1 1 1 We refer to small models as models with less than 1 billion parameters (hereinafter dubbed as 1B-models). We will delve into these paradigms and delineate their associated challenges as follows.

The most immediate way to perform Multimodal-CoT is to transform the input of different modalities into a unified modality and prompt LLMs to perform CoT ( Zhang et al., 2023a ; Lu et al., 2023 ; Liu et al., 2023 ; Alayrac et al., 2022 ; Hao et al., 2022 ; Yasunaga et al., 2022 ) . For example, it is possible to generate a caption for an image by a captioning model and then concatenate the caption with the original language input to be fed into LLMs ( Lu et al., 2022a ) . The development of large multimodal models such as GPT-4V ( OpenAI, 2023 ) and Gemini ( Reid et al., 2024 ) has notably enhanced the quality of generated captions, resulting in finer-grained and more detailed descriptions. However, the captioning process still incurs significant information loss when transforming vision signals into textual descriptions. Consequently, using image captions rather than vision features may suffer from a lack of mutual synergy in the representation space of different modalities. In addition, LLMs either have paywalls or resource-consuming to deploy locally.

To facilitate the interaction between modalities, another potential solution is to fine-tune smaller language models (LMs) by fusing multimodal features ( Zhang et al., 2023c ; Zhao et al., 2023 ) . As this approach allows the flexibility of adjusting model architectures to incorporate multimodal features, we study fine-tuning models in this work instead of prompting LLMs. The key challenge is that language models under 100 billion parameters tend to generate hallucinated rationales that mislead the answer inference ( Ho et al., 2022 ; Magister et al., 2022 ; Ji et al., 2022 ; Zhang et al., 2023b ) .

To mitigate the challenge of hallucination, we propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework that separates rationale generation and answer inference. 2 2 2 This work focuses on the language and vision modalities. In this way, answer inference can leverage better generated rationales that are based on multimodal information. Our experiments were conducted on the ScienceQA ( Lu et al., 2022a ) and A-OKVQA ( Schwenk et al., 2022 ) datasets, which are the latest multimodal reasoning benchmarks with annotated reasoning chains.

Our method achieves state-of-the-art performance on the ScienceQA benchmark upon the release. We find that Multimodal-CoT is beneficial in mitigating hallucination and boosting convergence. Our contributions are summarized as follows:

(i) To the best of our knowledge, this work is the first to study CoT reasoning in different modalities in scientific peer-reviewed literature.

(ii) We propose a two-stage framework by fine-tuning language models to fuse vision and language representations to perform Multimodal-CoT. The model is able to generate informative rationales to facilitate inferring final answers.

(iii) We elicit the analysis of why the naive way of employing CoT fails in the context and how incorporating vision features alleviates the problem. The approach has been shown to be generally effective across tasks and backbone models.

## 2 Background

This section reviews studies eliciting CoT reasoning by prompting and fine-tuning language models.

### 2.1 CoT Reasoning with LLMs

Recently, CoT has been widely used to elicit the multi-step reasoning abilities of LLMs ( Wei et al., 2022b ) . Concretely, CoT techniques encourage the LLM to generate intermediate reasoning chains for solving a problem. Studies have shown that LLMs can perform CoT reasoning with two major paradigms of techniques: Zero-Shot-CoT ( Kojima et al., 2022 ) and Few-Shot-CoT ( Wei et al., 2022b ; Zhang et al., 2023d ) . For Zero-Shot-CoT, Kojima et al. (2022) showed that LLMs are decent zero-shot reasoners by adding a prompt like “Let’s think step by step” after the test question to invoke CoT reasoning. For Few-Shot-CoT, a few step-by-step reasoning demonstrations are used as conditions for inference. Each demonstration has a question and a reasoning chain that leads to the final answer. The demonstrations are commonly obtained by hand-crafting or automatic generation. These two techniques, hand-crafting and automatic generation are thus referred to as Manual-CoT ( Wei et al., 2022b ) and Auto-CoT ( Zhang et al., 2023d ) .

With effective demonstrations, Few-Shot-CoT often achieves stronger performance than Zero-Shot-CoT and has attracted more research interest. Therefore, most recent studies focused on how to improve Few-Shot-CoT. Those studies are categorized into two major research lines: (i) optimizing the demonstrations; (ii) optimizing the reasoning chains. Table 1 compares typical CoT techniques.

#### Optimizing Demonstrations

The performance of Few-Shot-CoT relies on the quality of demonstrations. As reported in Wei et al. (2022b) , using demonstrations written by different annotators results in dramatic accuracy disparity in reasoning tasks. Beyond hand-crafting the demonstrations, recent studies have investigated ways to optimize the demonstration selection process. Notably, Rubin et al. (2022) retrieved the semantically similar demonstrations with the test instance. However, this approach shows a degraded performance when there are mistakes in the reasoning chains ( Zhang et al., 2023d ) . To address the limitation, Zhang et al. (2023d) found that the key is the diversity of demonstration questions and proposed Auto-CoT: (i) partition questions of a given dataset into a few clusters; (ii) sample a representative question from each cluster and generate its reasoning chain using Zero-Shot-CoT with simple heuristics. In addition, reinforcement learning (RL) and complexity-based selection strategies were proposed to obtain effective demonstrations. Fu et al. (2022) chose examples with complex reasoning chains (i.e., with more reasoning steps) as the demonstrations. Lu et al. (2022b) trained an agent to find optimal in-context examples from a candidate pool and maximize the prediction rewards on given training examples when interacting with GPT-3.5.

#### Optimizing Reasoning Chains

A notable way to optimize reasoning chains is problem decomposition. Zhou et al. (2022) proposed least-to-most prompting to decompose complex problems into sub-problems and then solve these sub-problems sequentially. As a result, solving a given sub-problem is facilitated by the answers to previously solved sub-problems. Similarly, Khot et al. (2022) used diverse decomposition structures and designed different prompts to answer each sub-question. In addition to prompting the reasoning chains as natural language texts, Chen et al. (2022) proposed program-of-thoughts (PoT), which modeled the reasoning process as a program and prompted LLMs to derive the answer by executing the generated programs. Another trend is to vote over multiple reasoning paths for a test question. Wang et al. (2022b) introduced a self-consistency decoding strategy to sample multiple outputs of LLMs and then took a majority over the final answers. Wang et al. (2022c) and Li et al. (2022c) introduced randomness in the input space to produce more diverse outputs for voting.

### 2.2 Eliciting CoT Reasoning by Fine-Tuning Models

A recent interest is eliciting CoT reasoning by fine-tuning language models. Lu et al. (2022a) fine-tuned the encoder-decoder T5 model on a large-scale dataset with CoT annotations. However, a dramatic performance decline is observed when using CoT to infer the answer, i.e., generating the reasoning chain before the answer (reasoning). Instead, CoT is only used as an explanation after the answer. Magister et al. (2022) and Ho et al. (2022) employed knowledge distillation by fine-tuning a student model on the chain-of-thought outputs generated by a larger teacher model. Wang et al. (2022a) proposed an iterative context-aware prompting approach to dynamically synthesize prompts conditioned on the current step’s contexts.

There is a key challenge in training 1B-models to be CoT reasoners. As observed by Wei et al. (2022b) , models under 100 billion parameters tend to produce illogical CoT that leads to wrong answers. In other words, it might be harder for 1B-models to generate effective CoT than directly generating the answer. It becomes even more challenging in a multimodal setting where answering the question also requires understanding the multimodal inputs. In the following part, we will explore the challenge of Multimodal-CoT and investigate how to perform effective multi-step reasoning.

## 3 Challenge of Multimodal-CoT

Existing studies have suggested that the CoT reasoning ability may emerge in language models at a certain scale, e.g., over 100 billion parameters ( Wei et al., 2022a ) . However, it remains an unresolved challenge to elicit such reasoning abilities in 1B-models, let alone in the multimodal scenario. This work focuses on 1B-models as they can be fine-tuned and deployed with consumer-grade GPUs (e.g., 32G memory). In this section, we will investigate why 1B-models fail at CoT reasoning and study how to design an effective approach to overcome the challenge.

### 3.1 Towards the Role of CoT

To begin with, we fine-tune a text-only baseline for CoT reasoning on the ScienceQA benchmark ( Lu et al., 2022a ) . We adopt FLAN-Alpaca Base {}_{\texttt{Base}} as the backbone language model. 3 3 3 https://github.com/declare-lab/flan-alpaca . It is a 200M T5 model ( Raffel et al., 2020 ) fine-tuned on Stanford Alpaca data ( Taori et al., 2023 ) . Implementation details are presented in Section 5.2 . Our task is modeled as a text generation problem, where the model takes the textual information as the input and generates the output sequence that consists of the rationale and the answer.

As an example shown in Figure 1 , the model takes the concatenation of tokens of the question text (Q), the context text (C), and multiple options (M) as the input. To study the effect of CoT, we compare the performance with three variants: (i) No-CoT which predicts the answer directly (QCM → \rightarrow A); (ii) Reasoning where answer inference is conditioned to the rationale (QCM → \rightarrow RA); (iii) Explanation where the rationale is used for explaining the answer inference (QCM → \rightarrow AR).

Surprisingly, as shown in Table 2 , we observe a ↓ \downarrow 12.31% accuracy decrease (81.63% → \rightarrow 69.32%) if the model predicts rationales before answers (QCM → \rightarrow RA). The results imply that the rationales might not necessarily contribute to predicting the right answer. According to Lu et al. (2022a) , the plausible reason might be that the model exceeds the maximum token limits before obtaining the required answer or stops generating the prediction early. However, we find that the maximum length of the generated outputs (RA) is always less than 400 tokens, which is below the length limit of language models (i.e., 512 in T5 models). Therefore, it deserves a more in-depth investigation into why the rationales harm answer inference.

### 3.2 Misleading by Hallucinated Rationales

To dive into how the rationales affect the answer prediction, we separate the CoT problem into two stages, rationale generation and answer inference . 4 4 4 The details will be presented in Section 4 . We report the RougeL score and accuracy for the rationale generation and answer inference, respectively. Table 3 shows the results based on the two-stage framework. Although the two-stage baseline model achieves a 90.73 RougeL score of the rationale generation, the answer inference accuracy is only 78.57%. Compared with the QCM → \rightarrow A variant (81.63%) in Table 2 , the result shows that the generated rationale in the two-stage framework does not improve answer accuracy.

Then, we randomly sample 50 error cases and find that the model tends to generate hallucinated rationales that mislead the answer inference. As an example shown in Figure 2 , the model (left part) hallucinates that, “ The south pole of one magnet is closest to the south pole of the other magnet ”, due to the lack of reference to the vision content. We find that such mistakes occur at a ratio of 56% among the error cases (Figure 3 (a)).

### 3.3 Multimodality Contributes to Effective Rationales

We speculate that such a phenomenon of hallucination is due to a lack of necessary vision contexts for performing effective Multimodal-CoT. To inject vision information, a simple way is to transform the image into a caption ( Lu et al., 2022a ) and then append the caption in the input of both stages.

However, as shown in Table 3 , using captions only yields marginal performance gains ( ↑ \uparrow 0.80%). Then, we explore an advanced technique by incorporating vision features into the language model. Concretely, we feed the image to the ViT model ( Dosovitskiy et al., 2021b ) to extract vision features. Then we fuse the vision features with the encoded language representations before feeding the decoder (more details will be presented in Section 4 ). Interestingly, with vision features, the RougeL score of the rationale generation has boosted to 93.46% (QCM → \rightarrow R), which correspondingly contributes to better answer accuracy of 85.31% (QCMR → \rightarrow A).

With those effective rationales, the phenomenon of hallucination is mitigated — 60.7% hallucination mistakes in Section 3.2 have been corrected (Figure 3 (b)), as an example shown in Figure 2 (right part). 5 5 5 The left mistakes are mainly about map understanding, requiring extra commonsense signals (Section 6.7 ). The analysis so far compellingly shows that vision features are indeed beneficial for generating effective rationales and contributing to accurate answer inference. As the two-stage method achieves better performance than one-stage methods, we choose the two-stage method in our Multimodal-CoT framework.

## 4 Multimodal-CoT

In light of the discussions in Section 3 , we propose Multimodal-CoT. The key motivation is the anticipation that the answer inference can leverage better generated rationales that are based on multimodal information. In this section, we will overview the procedure of the framework and elaborate on the technical design of the model architecture.

### 4.1 Framework Overview

Multimodal-CoT consists of two operation stages: (i) rationale generation and (ii) answer inference. Both stages share the same model structure but differ in the input X X and output Y Y . The overall procedure is illustrated in Figure 4 . We will take vision-language as an example to show how Multimodal-CoT works.

In the rationale generation stage, we feed the model with X = { X language 1 , X vision } X=\{X_{\textrm{language}}^{1},X_{\textrm{vision}}\} where X language 1 X_{\textrm{language}}^{1} represents the language input in the first stage and X vision X_{\textrm{vision}} represents the vision input, i.e., the image. For example, X X can be instantiated as a concatenation of question, context, and options of a multiple choice reasoning problem ( Lu et al., 2022a ) as shown in Figure 4 . The goal is to learn a rationale generation model R = F ⁡ ( X ) {R}=F(X) where R {R} is the rationale.

In the answer inference stage, the rationale R R is appended to the original language input X language 1 X_{\textrm{language}}^{1} to construct the language input in the second stage, X language 2 = X language 1 ∘ R X_{\textrm{language}}^{2}=X_{\textrm{language}}^{1}\circ{R} where ∘ \circ denotes concatenation. Then, we feed the updated input X ′ = { X language 2 , X vision } X^{\prime}=\{X_{\textrm{language}}^{2},X_{\textrm{vision}}\} to the answer inference model to infer the final answer A = F ⁡ ( X ′ ) A=F(X^{\prime}) .

In both stages, we train two models with the same architecture independently. They take the annotated elements (e.g., X → R X\rightarrow R , X ​ R → A XR\rightarrow A , respectively) from the training set for supervised learning. During inference, given X X , the rationales for the test sets are generated using the model trained in the first stage; they are used in the second stage for answer inference.

### 4.2 Model Architecture

Given language input X language ∈ { X language 1 , X language 2 } X_{\textrm{language}}\in\{X_{\textrm{language}}^{1},X_{\textrm{language}}^{2}\} and vision input X vision X_{\textrm{vision}} , we compute the probability of generating target text Y Y (either the rationale or the answer in Figure 4 ) of length N N by p ⁡ ( Y | X language , X vision ) = ∏ i = 1 N p θ ​ ( Y i ∣ X language , X vision , Y < i ) , p(Y|X_{\textrm{language}},X_{\textrm{vision}})=\prod_{i=1}^{N}p_{\theta}\left(Y_{i}\mid X_{\textrm{language}},X_{\textrm{vision}},Y_{<i}\right), (1) where p θ ​ ( Y i ∣ X language , X vision , Y < i ) p_{\theta}\left(Y_{i}\mid X_{\textrm{language}},X_{\textrm{vision}},Y_{<i}\right) is implemented with a Transformer-based network ( Vaswani et al., 2017 ) . The network has three major procedures: encoding, interaction, and decoding. Specifically, we feed the language text into a Transformer encoder to obtain a textual representation, which is interacted and fused with the vision representation before being fed into the Transformer decoder.

#### Encoding

The model F ⁡ ( X ) F(X) takes both the language and vision inputs and obtains the text representation H language H_{\textrm{language}} and the image feature H vision H_{\textrm{vision}} by the following functions: H language \displaystyle H_{\textrm{language}} = \displaystyle= LanguageEncoder ​ ( X language ) , \displaystyle\textrm{LanguageEncoder}(X_{\textrm{language}}), (2) H vision \displaystyle H_{\textrm{vision}} = \displaystyle= W h ⋅ VisionExtractor ​ ( X vision ) , \displaystyle W_{h}\cdot\textrm{VisionExtractor}(X_{\textrm{vision}}), (3) where LanguageEncoder( ⋅ \cdot ) is implemented as a Transformer model. We use the hidden states of the last layer in the Transformer encoder as the language representation H language ∈ ℝ n × d H_{\textrm{language}}\in\mathbb{R}^{n\times d} where n n denotes the length of the language input, and d d is the hidden dimension. Meanwhile, VisionExtractor( ⋅ \cdot ) is used to vectorize the input image into vision features. Inspired by the recent success of Vision Transformers ( Dosovitskiy et al., 2021a ) , we fetch the patch-level features by frozen vision extraction models, such as ViT ( Dosovitskiy et al., 2021b ) . After obtaining the patch-level vision features, we apply a learnable projection matrix W h W_{h} to convert the shape of VisionExtractor ​ ( X vision ) \textrm{VisionExtractor}(X_{\textrm{vision}}) into that of H language H_{\textrm{language}} ; thus we have H vision ∈ ℝ m × d H_{\textrm{vision}}\in\mathbb{R}^{m\times d} where m m is the number of patches.

Note that our approach is general to both scenarios with or without image context. For the questions without associated images, we use all-zero vectors as the “blank features” with the same shape as the normal image features to tell the model to ignore them.

#### Interaction

After obtaining language and vision representations, we use a single-head attention network to correlate text tokens with image patches, where the query ( Q {Q} ), key ( K {K} ) and value ( V {V} ) are H language H_{\textrm{language}} , H vision H_{\textrm{vision}} and H vision H_{\textrm{vision}} , respectively. The attention output H vision attn ∈ ℝ n × d H_{\textrm{vision}}^{\textrm{attn}}\in\mathbb{R}^{n\times d} is defined as: H vision attn = Softmax ​ ( Q ​ K ⊤ d k ) ​ V , H_{\textrm{vision}}^{\textrm{attn}}=\textrm{Softmax}(\frac{{Q}{{K}^{\top}}}{\sqrt{d_{k}}}){V}, where d k d_{k} is the same as the dimension of H language H_{\textrm{language}} because a single head is used.

Then, we apply the gated fusion mechanism ( Zhang et al., 2020 ; Wu et al., 2021 ; Li et al., 2022a ) to fuse H language H_{\textrm{language}} and H vision H_{\textrm{vision}} . The fused output H fuse ∈ ℝ n × d H_{\textrm{fuse}}\in\mathbb{R}^{n\times d} is obtained by: λ \displaystyle\lambda = \displaystyle= Sigmoid ​ ( W l ​ H language + W v ​ H vision attn ) , \displaystyle\textrm{Sigmoid}(W_{l}{H_{\textrm{language}}}+W_{v}{H_{\textrm{vision}}^{\textrm{attn}}}), (4) H fuse \displaystyle H_{\textrm{fuse}} = \displaystyle= ( 1 − λ ) ⋅ H language + λ ⋅ H vision attn , \displaystyle(1-\lambda)\cdot H_{\textrm{language}}+\lambda\cdot H_{\textrm{vision}}^{\textrm{attn}}, (5) where W l W_{l} and W v W_{v} are learnable parameters.

#### Decoding

Finally, the fused output H fuse H_{\textrm{fuse}} is fed into the Transformer decoder to predict the target Y Y .

## 5 Experiments

This section will present the benchmark dataset, the implementation of our technique, and the baselines for comparisons. Then, we will report our main results and findings.

### 5.1 Dataset

Our method is evaluated on the ScienceQA ( Lu et al., 2022a ) and A-OKVQA ( Schwenk et al., 2022 ) benchmark datasets. We choose those datasets because they are latest multimodal reasoning benchmarks with annotated reasoning chains. ScienceQA is a large-scale multimoda science question dataset with annotated lectures and explanations. It contains 21 k k multimodal multiple choice questions with rich domain diversity across 3 subjects, 26 topics, 127 categories, and 379 skills. There are 12 k k , 4 k k , and 4 k k questions in the training, validation, and test splits, respectively. A-OKVQA is a knowledge-based visual question answering benchmark, which has 25 k k questions requiring a broad base of commonsense and world knowledge to answer. It has 17 k k /1 k k /6 k k questions for train/val/test. As A-OKVQA provides multiple-choice and direct answer evaluation settings, we use the multiple-choice setting to keep consistency with ScienceQA.

### 5.2 Implementation

The following part presents the experimental settings of Multimodal-CoT and the baseline methods.

#### Experimental Settings

We adopt the T5 encoder-decoder architecture ( Raffel et al., 2020 ) under Base (200M) and large (700M) settings in our framework. We apply FLAN-Alpaca to initialize our model weights. 6 6 6 https://github.com/declare-lab/flan-alpaca . We will show that Multimodal-CoT is generally effective with other backbone LMs, such as UnifiedQA ( Khashabi et al., 2020 ) and FLAN-T5 ( Chung et al., 2022 ) (Section 6.3 ). The vision features are obtained by the frozen ViT-large encoder ( Dosovitskiy et al., 2021b ) . We fine-tune the models up to 20 epochs, with a learning rate of 5e-5. The maximum input sequence length is 512. The batch size is 8. Our experiments are run on 8 NVIDIA Tesla V100 32G GPUs. More details are presented in Appendix B .

#### Baseline Models

We utilized three categories of methods as our baselines:

(i) Visual question answering (VQA) models, including MCAN ( Yu et al., 2019 ) , Top-Down ( Anderson et al., 2018 ) , BAN ( Kim et al., 2018 ) , DFAF ( Gao et al., 2019 ) , ViLT ( Kim et al., 2021 ) , Patch-TRM ( Lu et al., 2021 ) , and VisualBERT ( Li et al., 2019 ) . These VQA baselines take the question, context, and choices as textual input, while utilizing the image as visual input. They employ a linear classifier to predict the score distribution over the choice candidates.

(ii) LMs, including the text-to-text UnifiedQA model ( Khashabi et al., 2020 ) and few-shot learning LLMs (GPT-3.5, ChatGPT, GPT-4, and Chameleon ( Lu et al., 2023 ) ). UnifiedQA ( Khashabi et al., 2020 ) is adopted as it is the best fine-tuning model in Lu et al. (2022a) . UnifiedQA takes the textual information as the input and outputs the answer choice. The image is converted into a caption extracted by an image captioning model following Lu et al. (2022a) . UnifiedQA treats our task as a text generation problem. In Lu et al. (2022a) , it is trained to generate a target answer text, i.e., one of the candidate options. Then, the most similar option is selected as the final prediction to evaluate the question answering accuracy. For GPT-3.5 models ( Chen et al., 2020 ) , we use the text-davinci-002 and text-davinci-003 engines due to their strong performance. In addition, we also include the comparison with ChatGPT and GPT-4. The inference is based on the few-shot prompting, where two in-context examples from the training set are concatenated before the test instance. The few-shot demonstrations are the same as those in Lu et al. (2022a) .

(iii) Fine-tuned large vision-language model. We select the recently released LLaMA-Adapter ( Zhang et al., 2023a ) , LLaVA ( Liu et al., 2023 ) , and InstructBLIP ( Dai et al., 2023 ) as the competitive large vision-language baselines. For LLaMA-Adapter, the backbone model is the 7B LLaMA model fine-tuned with 52 k k self-instruct demonstrations. To adapt to our tasks, the model is further fine-tuned on the ScienceQA dataset.

### 5.3 Main Results

Table 4 shows the main results in the ScienceQA benchmark. We observe that Mutimodal-CoT Large {}_{\texttt{Large}} achieves substantial performance gains over the prior best model in publications (86.54% → \rightarrow 90.45%). The efficacy of Multimodal-CoT is further supported by the results obtained from the A-OKVQA benchmark in Table 5 .

It is worth noting that Chameleon, LLaMA-Adapter, LLaVA, and InstructBLIP are concurrent works released several months after our work. In the subsequent Section 6.2 , we will show that our method is orthogonal to those multimodal models (e.g., InstructBLIP) and can be potentially used with them together to improve generality further, i.e., scaled to scenarios where human-annotated rationales are unavailable, thereby establishing the effectiveness across diverse tasks.

Ablation study results in Table 6 show that both the integration of vision features and the two-stage framework design contribute to the overall performance.

Furthermore, we find that Multimodal-CoT demonstrates the ability to mitigate hallucination (Section 3.3 ) and improve convergence (Section 6.1 ).

## 6 Analysis

The following analysis will first show that Multimodal-CoT helps enhance convergence speed and has the feasibility of adaptation to scenarios without human-annotated rationales. Then, we investigate the general effectiveness of Multimodal-CoT with different backbone models and vision features. We will also conduct an error analysis to explore the limitations to inspire future studies. We use models under the base size for analysis unless otherwise stated.

### 6.1 Multimodality Boosts Convergence

Figure 5 shows the validation accuracy curve of the baseline and Multimodal-CoT across different training epochs. “One-stage” is based on the QCM → \rightarrow A input-output format as it achieves the best performance in Table 2 and “Two-stage” is our two-stage framework. We find that the two-stage methods achieve relatively higher accuracy at the beginning than the one-stage baselines that generate the answer directly without CoT. However, without the vision features, the two-stage baseline could not yield better results as the training goes on due to low-quality rationales (as observed in Section 3 ). In contrast, using vision features helps generate more effective rationales that contribute to better answer accuracy in our two-stage multimodal variant.

### 6.2 When Multimodal-CoT Meets Large Models

A recent flame is to leverage large language models or large vision-language models to generate reasoning chains for multimodal question answering problems ( Zhang et al., 2023a ; Lu et al., 2023 ; Liu et al., 2023 ; Alayrac et al., 2022 ; Hao et al., 2022 ; Yasunaga et al., 2022 ) . We are interested in whether we can use large models to generate the rationales for Multimodal-CoT; thus breaking the need for datasets with human-annotated rationales. During the first-stage training of Multimodal-CoT, our target rationales are based on human annotation in the benchmark datasets. Now, we replace the target rationales with those generated ones. As ScienceQA contains questions with images and without images, we leverage InstructBLIP and ChatGPT to generate the rationales for questions with paired images and questions without paired images, respectively. 7 7 7 Examples are provided in Appendix C.1 . Then, we combine both of the generated pseudo-rationales as the target rationales for training (Multimodal-CoT w/ Generation) instead of relying on the human annotation of reasoning chains (Multimodal-CoT w/ Annotation).

Table 7 shows the comparison results. We see that using the generated rationales achieves comparable performance to using human-annotated rationales for training. In addition, the performance is also much better than directly prompting those baseline models to obtain the answer (in the QCM → \rightarrow A inference format).

We see that Multimodal-CoT can work effectively with large models. The findings above compellingly show the feasibility of adaptation to scenarios without human-annotated rationales, thereby establishing the effectiveness of our approach across diverse tasks.

### 6.3 Effectiveness Across Backbones

To test the generality of the benefits of our approach to other backbone models, we alter the underlying LMs to other variants in different types. As shown in Table 6.3 , our approach is generally effective for the widely used backbone models.

### 6.4 Using Different Vision Features

Different vision features may affect the model performance. We compare three widely-used types of vision features, ViT ( Dosovitskiy et al., 2021b ) , CLIP ( Radford et al., 2021 ) , DETR ( Carion et al., 2020 ) , and ResNet ( He et al., 2016 ) . ViT, CLIP, and DETR are patch-like features. For the ResNet features, we repeat the pooled features of ResNet-50 to the same length with the text sequence to imitate the patch-like features, where each patch is the same as the pooled image features. More details of the vision features are presented in Appendix B.1 .

Table 6.3 shows the comparative results of vision features. We observe that ViT achieves relatively better performance. Therefore, we use ViT by default in Multimodal-CoT.

### 6.5 Alignment Strategies for Multimodal Interaction

We are interested in whether using different alignment strategies for multimodal interaction may contribute to different behaviors of multimodal-CoT. To this end, we tried another alignment strategy, i.e., image-grounded text encoder, in BLIP Li et al. (2022b) . This alignment approach injects visual information by inserting one additional cross-attention layer between the self-attention layer and the feed-forward network for each transformer block of the text encoder. Our current strategy in the paper is similar to the unimodal encoder as in BLIP, which is used for comparison. In Table 10 , we see that using other alignment strategies also contributes to better performance than direct answering.

### 6.6 Generalization to Other Multimodal Reasoning Benchmarks

We are interested in evaluating the generalization capability of Multimodal-CoT to datasets outside its training domain. For this purpose, we utilize the widely-recognized multimodal reasoning benchmark, MMMU ( Yue et al., 2024 ) , and conduct an evaluation of Multimodal-CoT on MMMU without further training.

As shown in Table 11 , it is evident that Multimodal-CoT demonstrates effective generalization to MMMU, achieving better performance than various larger models around 8B.

### 6.7 Error Analysis

To gain deeper insights into the behavior of Multimodal-CoT and facilitate future research, we manually analyzed randomly selected examples generated by our approach. The categorization results are illustrated in Figure 6 . We examined 50 samples that yielded incorrect answers and categorized them accordingly. The examples from each category can be found in Appendix D .

The most prevalent error type is commonsense mistakes, accounting for 80% of the errors. These mistakes occur when the model is faced with questions that require commonsense knowledge, such as interpreting maps, counting objects in images, or utilizing the alphabet. The second error type is logical mistakes, constituting 14% of the errors, which involve contradictions in the reasoning process. Additionally, we have observed cases where incorrect answers are provided despite the CoT being either empty or correct, amounting to 6% of the errors. The CoT in these cases may not necessarily influence the final answer.

The analysis reveals potential avenues for future research. Enhancements can be made to Multimodal-CoT by: (i) integrating more informative visual features and strengthening the interaction between language and vision to enable comprehension of maps and numerical counting; (ii) incorporating commonsense knowledge; and (iii) implementing a filtering mechanism, such as using only relevant CoTs to infer answers and disregarding irrelevant ones.

## 7 Conclusion

This paper formally studies the problem of multimodal CoT. We propose Multimodal-CoT that incorporates language and vision modalities into a two-stage framework that separates rationale generation and answer inference, so answer inference can leverage better generated rationales from multimodal information. With Multimodal-CoT, our model under 1 billion parameters achieves state-of-the-art performance on the ScienceQA benchmark. Analysis shows that Multimodal-CoT has the merits of mitigating hallucination and enhancing convergence speed. Our error analysis identifies the potential to leverage more effective vision features, inject commonsense knowledge, and apply filtering mechanisms to improve CoT reasoning in future studies.

## Appendix A Extended Analysis for the Challenge of Multimodal-CoT

### A.1 Additional Examples of Misleading through Hallucinated Rationales

Based on our case studies (Section 3.2), we have observed a tendency for the baseline model to generate hallucinated rationales. Here, we present additional examples to illustrate this phenomenon, as depicted in Figure 7 .

### A.2 Two-Stage Training Performance with Different Sizes of LMs

In Section 3, we observed that the inclusion of vision features has a positive impact on the generation of more effective rationales, consequently resulting in improved answer accuracy. In addition to incorporating vision features, another approach to addressing the issue of incorrect rationales is to scale the size of the language model (LM). Figure 8 showcases the answer accuracy achieved by our two-stage training framework, both with and without the integration of vision features. Notably, when employing a larger LM, the baseline accuracy (without vision features) experiences a significant enhancement. This finding suggests that scaling the LM size could potentially alleviate the problem of incorrect rationales. However, it is crucial to acknowledge that the performance still falls considerably short of utilizing vision features. This outcome further validates the effectiveness of our Multimodal-CoT methodology across varying LM sizes.

### A.3 Discussion of the Possible Paradigms to Achieve Multimodal-CoT

As discussed in Section 1, there are two primary approaches to facilitate Multimodal-CoT reasoning: (i) prompting LLMs and (ii) fine-tuning small models. The common approach in the first approach is to unify the input from different modalities and prompt LLMs to perform reasoning ( Zhang et al., 2023a ; Lu et al., 2023 ; Liu et al., 2023 ; Alayrac et al., 2022 ; Hao et al., 2022 ; Yasunaga et al., 2022 ) . For instance, one way to achieve this is by extracting the caption of an image using a captioning model and then concatenating the caption with the original language input to feed LLMs. By doing so, visual information is conveyed to LLMs as text, effectively bridging the gap between modalities. This approach can be represented as the input-output format <image → \rightarrow caption, question + caption → \rightarrow answer>. We refer to this approach as Caption-based Reasoning (Figure 9 a). It is worth noting that the effectiveness of this approach depends on the quality of the image caption, which may be susceptible to errors introduced during the transfer from image captioning to answer inference.

In contrast, an intriguing aspect of CoT is the ability to decompose complex problems into a series of simpler problems and solve them step by step. This transformation leads to a modification of the standard format <question → \rightarrow answer> into <question → \rightarrow rationale → \rightarrow answer>. Rationales, being more likely to reflect the reasoning processes leading to the answer, play a crucial role in this paradigm. Consequently, we refer to approaches following this paradigm as CoT-based Reasoning . The nomenclature has been widely adopted in the literature ( Huang & Chang, 2022 ; Zhang et al., 2023d ; Lu et al., 2022c ) .

Our work aligns with the paradigms of CoT-based Reasoning in the context of multimodal scenarios, specifically employing the <question + image → \rightarrow rationale → \rightarrow answer> framework (Figure 9 b). This approach confers advantages on two fronts. Firstly, the Multimodal-CoT framework leverages feature-level interactions between vision and language inputs, enabling the model to gain a deeper understanding of the input information and facilitating more effective inference of answers by incorporating well-founded rationales. Our analysis has demonstrated that Multimodal-CoT offers notable benefits by mitigating hallucination and enhancing convergence, resulting in superior performance on our benchmark datasets. Secondly, the lightweight nature of Multimodal-CoT renders it compatible with resource constraints and circumvents any potential paywalls.

## Appendix B Experimental Details

### B.1 Details of Vision Features

In Section 6.2, we compared four types of vision features, ViT ( Dosovitskiy et al., 2021b ) , CLIP ( Radford et al., 2021 ) , DETR ( Carion et al., 2020 ) , and ResNet ( He et al., 2016 ) . The specific models are: (i) ViT: vit_large_patch32_384 , 8 8 8 https://github.com/rwightman/pytorch-image-models . (ii) CLIP: RN101; 9 9 9 https://github.com/jianjieluo/OpenAI-CLIP-Feature . (iii) DETR: detr_resnet101_dc5 ; 10 10 10 https://github.com/facebookresearch/detr . (iv) ResNet: we use the averaged pooled features of a pre-trained ResNet50 CNN.

Table 12 presents the dimension of the vision features (after the function VisionExtractor( ⋅ \cdot ) in Eq. 3). For ResNet-50, we repeat the pooled features of ResNet-50 to the same length as the text sequence to imitate the patch-like features, where each patch is the same as the pooled image features.

### B.2 Datasets

Our method is evaluated on the ScienceQA ( Lu et al., 2022a ) and A-OKVQA ( Schwenk et al., 2022 ) benchmark datasets.

∙ \bullet ScienceQA is a large-scale multimodal science question dataset with annotated lectures and explanations. It contains 21 k k multimodal multiple choice questions with rich domain diversity across 3 subjects, 26 topics, 127 categories, and 379 skills. The dataset is split into training, validation, and test splits with 12 k k , 4 k k , and 4 k k questions, respectively.

∙ \bullet A-OKVQA is a knowledge-based visual question answering benchmark, which has 25 k k questions requiring a broad base of commonsense and world knowledge to answer. Each question is annotated with rationales that explain why a particular answer was correct according to necessary facts or knowledge. It has 17 k k /1 k k /6 k k questions for train/val/test.

For ScienceQA, our model is evaluated on the test set. For A-OKVQA, our model is evaluated on the validation set as the test set is hidden.

### B.3 Implementation Details of Multimodal-CoT

As the Multimodal-CoT task requires generating the reasoning chains and leveraging the vision features, we adopt the T5 encoder-decoder architecture ( Raffel et al., 2020 ) under Base (200M) and large (700M) settings in our framework. We apply FLAN-Alpaca to initialize our model weights. 11 11 11 https://github.com/declare-lab/flan-alpaca . We will show that Multimodal-CoT is generally effective with other backbone LMs, such as UnifiedQA ( Khashabi et al., 2020 ) and FLAN-T5 ( Chung et al., 2022 ) (Section 6.1). The vision features are obtained by the frozen ViT-large encoder ( Dosovitskiy et al., 2021b ) . Since using image captions can slightly improve model performance, as shown in Section 3.3, we append the image captions to the context following Lu et al. (2022a) . The captions are generated by InstructBLIP ( Dai et al., 2023 ) . We fine-tune the models up to 20 epochs, with a learning rate selected in {5e-5, 8e-5}. The maximum input sequence lengths for rationale generation and answer inference are 512 and 64, respectively. The batch size is 8. Our experiments are run on 8 NVIDIA Tesla V100 32G GPUs.

## Appendix C Further Analysis

### C.1 Examples of Rationale Generation with Large Models

A recent flame is to leverage large language models or large vision-language models to generate reasoning chains for multimodal question answering problems ( Zhang et al., 2023a ; Lu et al., 2023 ; Liu et al., 2023 ; Alayrac et al., 2022 ; Hao et al., 2022 ; Yasunaga et al., 2022 ) . We are interested in whether we can use large models to generate the rationales for Multimodal-CoT; thus breaking the need for datasets with human-annotated rationales. During the first-stage training of Multimodal-CoT, our target rationales are based on human annotation in the benchmark datasets. Now, we replace the target rationales with those generated by an LLM or a vision-language model. Concretely, we feed the questions with images (IMG) and the question without images (TXT) to InstructBLIP ( Dai et al., 2023 ) (Figure 10 a) and ChatGPT (Figure 10 b) for zero-shot inference, respectively. Then, we use the generated pseudo-rationales as the target rationales for training instead of relying on the human annotation of reasoning chains.

### C.2 Detailed Results of Multimodal-CoT on Different Backbone Models

To test the generality of the benefits of our approach to other backbone models, we alter the underlying LMs to other variants of different types. As detailed results shown in Table 13 , our approach is generally effective for the widely used backbone models.

## Appendix D Examples of Case Studies

To gain deeper insights into the behavior of Multimodal-CoT and facilitate future research, we manually analyzed randomly selected examples generated by our approach. The categorization results are illustrated in Figure 11 . We examined 50 samples that yielded incorrect answers and categorized them accordingly.

The most prevalent error type is commonsense mistakes, accounting for 80% of the errors. These mistakes occur when the model is faced with questions that require commonsense knowledge, such as interpreting maps (Figure 12 a), counting objects in images (Figure 12 b), or utilizing the alphabet (Figure 12 c).

The second error type is logical mistakes, constituting 14% of the errors, which involve comparison mistakes (Figure 13 a) and contradictions in the reasoning process (Figure 13 b).

Additionally, we have observed cases where incorrect answers are provided despite the CoT being either empty or correct, amounting to 6% of the errors. The CoT in these cases may not necessarily influence the final answer (Figure 14 ).

The analysis reveals potential avenues for future research. Enhancements can be made to Multimodal-CoT by: (i) integrating more informative visual features and strengthening the interaction between language and vision to enable comprehension of maps and numerical counting; (ii) incorporating commonsense knowledge; and (iii) implementing a filtering mechanism, such as using only relevant CoTs to infer answers and disregarding irrelevant ones.

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