Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models

Authors: Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: large language model, question answering, chain-of-thought

Abstract:
We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key contribution is a novel reasoning-retrieval mechanism that decomposes a complex question into a reasoning chain via systematic prompting and pre-designed actions.  Methodologically, we propose three types of domain-adaptable `Plug-and-Play'  actions for retrieving real-time information from heterogeneous sources. We also propose a multi-reference faith score to verify conflicts in the answers.
In addition, our system demonstrates that detecting the knowledge boundaries of LLMs can significantly reduce both LLM interaction frequency and tokens usage in QA tasks. Empirically, we exploit both public benchmarks and a Web3 case study to demonstrate the capability of CoA over other methods.

Decision: accept (poster)
Reviewer scores: 8, 8, 5

Reviews and discussion:
<thread>
creation date:
1730376572009

thread_type:
official_review

<thread_object>
creator: reviewer_vgsf

<content>
summary:
The paper presents the Chain-of-Action (CoA) framework designed to improve large language models' (LLMs) performance in multimodal and retrieval-augmented question-answering (QA). CoA addresses challenges such as hallucination and weak compositional reasoning by decomposing questions into reasoning chains. This method incorporates plug-and-play actions for retrieving diverse data sources and uses a novel multi-reference faith score for verification. Empirical results show CoA outperforms other methods in public benchmarks and real-world applications.

strengths:
- **Innovative Framework**: The CoA's structured decomposition into sub-questions and its use of domain-adaptable plug-and-play actions represent a significant advancement in enhancing the faithfulness and accuracy of LLM responses.
- **Empirical Validation**: Demonstrated strong performance on benchmarks and real-world applications, notably outperforming existing baselines in multimodal QA tasks.
- **Verification Mechanism**: The multi-reference faith score is an effective metric for cross-validating LLM-generated answers against external sources, enhancing reliability.
- **Practical Impact**: Real-world implementation in a Web3 QA system showed increased user engagement and positive feedback, validating the method's applicability.

weaknesses:
- While the CoA approach shows strong empirical performance, its adaptability to more diverse or unstructured data modalities beyond text and tabular data remains to be proven.
- The scalability and efficiency when integrating more complex or real-time data sources require further exploration, especially in scenarios with rapidly changing information.
- The approach, despite its modular design, may face challenges in tasks involving higher-order reasoning or complex multi-step dependencies that are not purely fact-based.

questions:
1. Can the authors provide more details on how the CoA framework could be adapted for tasks involving visual or mixed data modalities?
2. How does the framework handle discrepancies or conflicts when sources provide contradictory information?
3. Are there plans to explore CoA's performance in real-time, fast-evolving information retrieval scenarios where data may change rapidly (e.g., live news events)?
4. Could the use of CoA extend to tasks requiring intricate reasoning paths that involve recursive or nested logic?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730669974822

thread_type:
official_review

<thread_object>
creator: reviewer_wymh

<content>
summary:
The authors propose a new QA retrieval mechanism called Chain of Action(CoA). When a question is asked to an LLM, there is a prompt which generates a list of actions the LLM needs to take first to effectively answer the questions. They introduce a Plug and Play approach where in case of Multimodal LLMs, the actions taken can be integrated into the application. The actions can be web query or data analysis. The paper integrates 3 such actions. The LLM then performs each of the individual action generated and then there is another query which combines information from all the actions. The LLM then gives an answer based on the newly injected information

strengths:
1. The authors utilize newer multimodal LLM abilities to perform actions such as web query and data analysis. The authors come up with a new QA mechanism for LLMs which uses the actions The method is called Chain of Action(CoA).
2. The authors demonstrate that this method significantly outperforms other reasoning and QA methods on many QA datasets. 
3. The improvement of using actions over thoughts does seem to be the natural way of solving a question. This approach has significant potential for improving QA capabilities of LLMs.

weaknesses:
1. Based on the number of actions to be taken and what kind of "plug" is used for the action, the time taken to finish all actions and send out an answer might become significant. It would have been good to see the study on latency(eg. average response time) of the system because of the new method.
2. It would be helpful to conduct an ablation study when you remove specific action types to isolate their impact on performance. This would provide clearer insights on how much this method relies on additional capabilities. 
3. Comparing CoA with and without the ability to perform additional "plugs" across different types of questions can be useful in understanding the impact of this method.

questions:
1. Can you elaborate on the key differences between "thoughts" in CoT and "actions" in CoA? How does this change improve the overall performance? It would also be helpful if you can discuss the limitations and trade-offs between them.
2. If the system doesn't have the ability to add additional actions like web query, does CoA still perform better than CoT. 
3. Does CoA add significant latency to QA process?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730674239610

thread_type:
official_review

<thread_object>
creator: reviewer_mjpz

<content>
summary:
The paper introduces the Chain-of-Action (CoA) framework, a novel approach to multimodal and retrieval-augmented question answering that enhances the faithfulness and reasoning quality of large language models (LLMs). CoA addresses key challenges in QA, such as unfaithful responses and weak reasoning, by decomposing questions into a series of reasoning steps or actions that systematically retrieve and verify information from various sources. The framework introduces three "Plug-and-Play" actions—web querying, knowledge encoding, and data analyzing—that support multimodal data integration. Additionally, a multi-reference faith score (MRFS) is proposed to resolve inconsistencies and improve response accuracy. Experimental results demonstrate CoA’s effectiveness in handling complex questions across QA benchmarks and in real-world applications, particularly in the Web3 domain.

strengths:
1. This study introduces a framework embodying the divide-and-conquer approach, effectively breaking down complex tasks into manageable components that are tackled sequentially. This structure enhances the model's ability to handle multifaceted queries with improved precision.

2. The empirical results demonstrate notable improvements in both performance and efficiency, as reflected in reduced API calls and token usage compared to prior methods. These gains underscore the framework’s effectiveness and potential for cost-saving in real-world applications.

3. The introduction of the multi-reference faith score (MRFS) is a contribution, which effectively identifies and mitigates information conflicts, and improves answer reliability and trustworthiness in real-time applications.

weaknesses:
1. The paper’s primary weakness lies in how it presents its key concepts and narrative. Many claims, such as "multimodal," "plug-and-play," and "action-based" elements, lack direct evidence or clear definitions, making it challenging to follow the core contributions. Though the pipeline is straightforward, understanding the study's actual workflow is hindered by (1) inaccurate terminology, (2) loosely connected methodology descriptions, and (3) a mix of abstract workflows and technical details.

2. Certain terms are uncommon or seem misapplied, which leads to confusion. For example, terms like "multimodal" (when referring to text and tabular data), "chain-of-action" (more of a "chain-of-data-collection-and-verification"), "actions design" (data collection), "actions workflow" (data verification), "node" (sub-question), and "knowledge boundary" (what a model actually knows) lack clarity and could benefit from more precise definitions or alternatives.

3. Question decomposition appears critical to this framework, yet there is limited discussion on decomposition strategies or comparisons with existing baselines. Further elaboration here would strengthen the paper's contributions.

4. The "plug-and-play" feature is presented as a low-cost prompting strategy; however, integrating retrieval for each data type (e.g., web, internal knowledge) may not be straightforward. It may be worth reconsidering or refining this claim to better reflect its implementation complexity.

5. The paper’s claim of multimodal data handling is unclear. If the input consists of real-time information, domain knowledge, and tabular data, it may be more accurately described as handling heterogeneous data rather than multimodal data. Additionally, if tabular data is linearized as text for LLM input, the fundamental multimodal claim weakens.

6. The study does not include ablations to show the specific contribution of tabular data. Providing such analyses could clarify its impact on the framework's performance.

7. Section 3.2 mentions expert evaluation on a 1 to 3 scale based on three criteria, but it lacks details on the expert recruitment process, qualifications, and any inter-rater reliability metrics. Adding these details would increase the transparency and credibility of the evaluation process.

questions:
1. Could you clarify what “imputation” refers to in Table 2? Are there results available for CoA without MRFS, and what does “w/ ROUGE” mean? My understanding was that ROUGE is used only in ASQA.

2. In Table 3, could you provide separate statistics for input and output tokens, as well as the average token usage per action? This would help readers better understand the specific cost details.

3. Could you elaborate on what is meant by the term “knowledge boundary”?

4. Are the results of the Chain-of-Action framework directly comparable to previous studies? I noticed that this study used GPT-4, while DSP and SearchChain relied on older-generation LLMs (text-davinci-002 and gpt-3.5-turbo, respectively).

5. Would it be fair and perhaps clearer to rename Sections 2.2.1 and 2.2.2 as "Data Collection" and "Data Verification," instead of “Actions Design” and “Actions Workflow”? These alternative terms seem easier to understand and align well with the content of the corresponding subsections.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732147976636

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Rebuttal / Revision Response

comment:
Dear Reviewers,

We thank the reviewers for the insightful questions and reviews. Your time and effort dedicated to improving our work are truly appreciated.

We have done all the experiments suggested and answered all the questions. All modifications are marked in red color.

Major revisions include:

**1. New ablation study different action influence on CoA performance in Table 2**.  `reviewer wYMh` `reviewer mjpZ`

- Conducted an ablation study different action influence on CoA performance
- Results:   each module is important to the method performance and each module shows better performance than other baselines.

**2. New ablation study on Latency and LLM usage per question in Table 3.** `reviewer wYMh` `reviewer mjpZ`

- Conducted an ablation study to evaluate the CoA Latency and input and output tokens with baselines
- Results: our method show a less latency and less LLM usage compared with most of baselines.

**3. New experiment on performance without tabular data in Table 6.**  `reviewer mjpZ`

- Conducted an ablation study to performance without tabular data
- Results: From the updated results, we observe a significant decline in the coverage and overall quality metrics without the inclusion of real-time market prices, highlighting the lack of truly useful references. However, the non-redundancy and readability metrics show negligible differences.

**4. New expert recruitment process and questionaire in Appendix F**: additional expert evaluator for human evaluation `reviewer mjpZ`

**5. Revised abstract**:  make the description of the methodology more clear.   `reviewer mjpZ`

**6. Revised Caption in Figure 1**: `reviewer mjpZ`

**7 . Revised in Sec 1**: make the description of the methodology more clear and reclassify the contribution of our work `reviewer mjpZ`

**8. Revised in Sec 2**: make the description of the methodology more clear   `reviewer mjpZ`

**9. Revised metrics in Sec 3.1**: update the definition of ROUGE and ROUGE-L metric used in our paper `reviewer mjpZ`

**10. Revised Implmentation in Sec 3.1**:  refined the claim regarding the "plug-and-play" feature to provide a more accurate description  `reviewer mjpZ`

**11. Revised Caption in Table 3**: add the new experiment of the latency and input &ouput tokens of CoA  `reviewer wYMh`  `reviewer mjpZ`

**12. Revised Caption in Table 6**: add the new experiment of the performance without tabular action `reviewer mjpZ`


We hope these revisions address the reviewers’ concerns and improve the overall quality of our paper.

Thank you again for your review!

Best regards,

Authors

---

Below, we also summarize the key points in our responses:

### Key Points in Our Responses

**Reviewer mjpZ**

* We addressed the concern regarding the accuracy of terminology and methodology descriptions by refining ambiguous terms like "multimodal" and "actions workflow." For example, we replaced "multimodal data" with "heterogeneous data" and clarified the meaning of "knowledge boundary" in response to Question 3. These revisions aim to improve the paper's precision and align terminology with its intended meaning, ensuring clarity for readers.
* We clarified the decomposition strategies critical to our framework by comparing CoA with established baselines in Table 2. Additionally, Appendix D provides a case study to illustrate the distinctions between CoA and CoT. Our analysis of 50 Social QA dataset questions showed CoA achieved 96% correctness and 98% relevance, outperforming CoT. These updates demonstrate the framework's effectiveness and strengthen the contributions of the paper.
* We refined the description of the "plug-and-play" feature by specifying that, while pre-designed actions can be easily added or removed, integrating new actions requires careful design to maintain compatibility with input-output formats. This clarification, included in the introduction, provides a more accurate representation of the feature’s complexity while retaining its flexibility.
* We conducted an ablation study to evaluate the specific contribution of tabular data to our framework. The results, presented in Table 6, show that excluding tabular data reduces coverage and quality metrics, emphasizing its importance in retrieving real-time market data. However, non-redundancy and readability metrics were minimally affected. These findings clarify tabular data's role and address the concern about its impact.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper introduces the Chain-of-Action (CoA) framework, a new approach to improving multimodal and retrieval-augmented QA for LLMs. The framework tackles two core challenges in QA—unfaithful reasoning and information hallucination—by decomposing complex questions into sequential reasoning steps termed "actions." These actions include web querying, knowledge encoding, and data analysis, enabling systematic integration of heterogeneous data sources. Additionally, the authors propose the multi-reference faith score (MRFS), which cross-validates model outputs against multiple sources to improve reliability. Empirical results demonstrate CoA's effectiveness, showing improved performance across public QA benchmarks. The framework claims advantages in cost-efficiency (via reduced API calls) and flexibility, allowing integration of diverse data types.

Strenghts:
- CoA’s structured decomposition into sub-questions/actions represents a significant advancement in tackling complex QA tasks. Its modular design provides adaptability for various data types and retrieval methods. 
- The proposed framework consistently outperforms baseline methods (e.g., CoT, DSP, SearchChain) across multiple QA benchmarks, demonstrating its robustness and versatility. 
- The introduction of MRFS as a reliability metric enhances the trustworthiness of responses, mitigating common issues like hallucination and conflicting information. 
- CoA demonstrates efficiency in reducing token usage and API calls. This provides clear advantages for scalability in cost-sensitive deployments. 

Weaknesses:
The initial weaknesses of the paper raised by reviewers include the following:
- Terminology used in the paper, such as "multimodal," "plug-and-play," and "chain-of-action," are ambiguously defined or potentially misapplied (e.g., text and tabular data being labeled as "multimodal"). 
- The paper does not include sufficient ablation studies to isolate the contributions of individual components, such as the impact of MRFS or the use of tabular data in performance gains.
- While CoA reduces token usage, its modularity and additional retrieval steps may introduce significant latency, especially with complex or real-time data sources. The study lacks an analysis of response times and scalability for dynamic scenarios.
- The framework's applicability to non-textual or highly unstructured data, such as visual or live-stream data, remains unexplored, which limits its generalizability.

During the rebuttal stage, the authors revised the manuscript and addressed the majority of these weaknesses.

Overall, this paper makes a meaningful contribution in improving multimodal and retrieval-augmented QA for LLMs. The paper received strong ratings with at least two reviewers excited about the paper, and no reviewer opposing acceptance. After evaluating the discussions and the revision, to me it appears that the authors have adequately addressed the major concerns.