Web Retrieval Agents for Evidence-Based Misinformation Detection

Summary

This paper proposes an approach that combines web retrieval with LLMs for misinformation detection. The paper presents a range of experiments that varies different components of the pipeline. Overall this is an important direction of research, and the results are promising. However, I had trouble reading this paper as it does have severe presentational issues, partly due to the many analyses; they often lacked explanation, depth or certain choices were not clearly explained. Some models appeared in one analysis, but not in the other. A table is shown but barely explained (Table 7). Furthermore, some natural questions could have been pursued (see my comments) to shed further light on this topic, such as an in-depth analysis of the type of queries that the LLMs execute.

Rating

5

Confidence

4

Ethics flag

1

Reasons to accept

* Fact-checking is a very important and timely topic. Furthermore, (as the paper claims), retrieval-augmented LLM approaches have been barely explored so far, while they make perfect sense for this problem. * The paper includes a wide range of experiments (but this is also a weakness); I particularly liked the missing information analysis. * The paper could inspire future work.

Reasons to reject

* Presentational issues: This in itself is not a reason to reject the paper, but it did weaken the paper. As an example, the references miss parentheses throughout the text, which makes reading the paper more difficult. The paper also suddenly includes results with the Fever v2 dataset, but this was never introduced. See more comments below. * It is unclear how the prompts were developed. Furthermore, studies have shown that results can be sensitive to only small changes to the prompts (even small formatting changes); it’s therefore not clear to me how much the results would change with small prompt changes. * The main experiments use only one dataset (LIAR-New), and actually only a subset of this dataset. Given that misinformation is such a large topic with many datasets available, I would expect experiments on more datasets. Otherwise, the authors need to clearly explain why the other datasets are not suitable. Even if it’s the case that this dataset offers a unique benefit (e.g., labels for examples with missing context), other parts of the experiments can be performed on other datasets as well to strengthen the analysis. * I believe the paper would have been stronger if it would only focus on a few core analyses, rather than trying to include so many as it does now. In many cases, the analyses lack explanation or depth, or are not systematic enough (e.g., only performed on a subset of the models, without explaining why).

Questions to authors

Not necessarily questions, but some remaining comments: * What do you mean with HiSS being unstable? * Page 6 is hard to read due to the many tables (caption vs. text is not clear) * I wish the authors had done an analysis on the queries executed; this would really strengthen their argument about decomposition and highlight future directions of research. What queries are executed by the LLMs? Are they relevant? (basically: do they target the information that is needed?). Currently, it is also unclear where the errors come from. Are the queries not suitable, or does the LLM summarization part go wrong? * There are certain design changes that are not clearly explained. As an example, why use GPT-3.5 to summarize the prompt? Wouldn’t a more natural choice be to have the summarization model match the main LLM? Some details of the experiments are unclear: * You experiment with different summarization models in Table 5, but it’s unclear which LLM is used as the main model there. * The PolitiFact experiment is nice, but why did you only report results with GPT-3.5-turbo as the main model, while that’s a model where the improvement using search was already low (so then perhaps the effect of removing PolitiFact is also limited?). A more natural choice would be to do this for all models, or for the best one (gpt-4). * Causal Walk (Zhang et al. (2024)) is used as a baseline in Table 7, but this was never explained/introduced before. * The difference between the proposed approach and other baselines (e.g., HiSS, WikiChat etc.) can be explained more clearly.

Summary

This paper proposes a framework for misinformation detection based on agentic AI and LLMs, where LLMs are enabled to decide when to search for additional information and retrieve it through web search in an iterative process. The paper studies the impact of such framework over the LIAR dataset. It also studies the effect of the retrieval and summarization tools used as well as the relevance of the sources where additional information is retrieved from.

Rating

7

Confidence

4

Ethics flag

1

Reasons to accept

- Innovative approach combining agentic AI and RAG - Thorough analysis from different perspectives although I miss that the evaluation also addressed other datasets in addition to LIAR

Reasons to reject

- Evaluating on more evaluation datasets would have provided a wider perspective on the approach and its possible generalization

Summary

The paper examines the problem of misinformation detection through experiments that compares and combines various LLM models (e.g. GPT-4) with two RAG search systems (Cohere and DuckDuckGo). The paper evaluates the various system configurations they constructed primarily on the LIAR-New dataset which some additional experiments using the FEVER dataset. The paper finds that the best approach involves using a SOTA LLM like GTP-4 with prompted instructions to craft queries to submit to the RAG search systems and then analyzing the summarized RAG search results against the original information claim to make a final decision. Several interesting observations were made along the way, including one which indicate than open web searches do not see degradations in finding accurate/truthful information on the evaluation data sets even when reliable information sources like Politifact and Wikipedia are disabled in the search.

Rating

7

Confidence

3

Ethics flag

1

Reasons to accept

Very interesting paper. I enjoyed reading it. Solid experiments comparing numerous different system configurations and different forms of ablation.

Reasons to reject

I don't see any reason to reject. The approach taken is simple, yet effective and the experiments provide multiple comparative studies.

Summary

The paper presents a system that combined retrieval-augmented generation (RAG) with large language models (LLMs) for misinformation detection. The authors present a considerable amount of discussion and analysis, reporting significantly improved results on the LIAR-New corpus. The paper presents experiments supporting the flexibility of the presented pipeline, which includes search and summarization modules.

Rating

6

Confidence

4

Ethics flag

1

Reasons to accept

1. The overall contribution of RAG+LLM with the application being misinformation detection, is a rather novel contribution as an application. 2. Strong analysis and discussion (e.g., the analysis on the type of missing information is certainly a strength) and ablation studies (on the inclusion of Politifact and on the number of search results from DuckDuckGo). In particular, I liked the analysis of the summarizer's role in the pipeline. 3. A good discussion of limitations by models. 4. The inclusion of the analysis of bias and credibility of sources is a definite strength.

Reasons to reject

There are a few drawbacks, which I list and discuss here. Minor remarks and suggestions are provided separately as "questions to authors": 1. There are two or three places (Sec. 2, 4, and Appendix A.2) where the authors mention the instability of HiSS. However, I am not sure if there is enough evidence of instability demonstrated in the experimental results. The only indication provided is that the performance of HiSS is worse with GPT-4 than with GPT-3.5. But why is this "unstable"? On a related note, I don't understand why in the baseline models' results in Table 2, there are no errors/deviations reported (except for Cohere Chat with RAG). 2. Some of the auxiliary contributions do not seem novel. For example, suggesting that "no single source is necessary for accuracy, but it is important to have enough sources to reduce noise and improve the chances of finding the necessary evidence." (Sec. 4, "The Role of the Sources"). This observation has been made quite consistently since early work on credibility and truth discovery. On the other hand, this opens up questions about how to reconcile potentially conflicting evidence arriving from different sources. 3. It is not clear if the facts, as discussed in Sec. 3.2 are "atomic" claims or complex claims that be reduced to sub-claims. If they are not atomic claims, then the use of 0/1 binary labels is problematic. In the introduction, it is mentioned that the pipeline integrated techniques for claim decomposition, but I do not see any further details of this in the rest of the paper. 4. In 3.4, the use of macro-averaged F1 is justified. But why not report classwise precision and recall? That will give the readers a more detailed picture of the models' performances. 5. I would also like to point out that the use of Media Bias Fact Check (MBFC) as the only source for bias analysis is questionable. While this resource has been successfully used in NLP literature quite often, it has received severe criticism elsewhere, including journalism. Even Poynter Institute (the institute behind PolitiFact, which plays a key role in this paper) has criticized MBFC's methodology "is in no way scientific." (Funke and Mantzarlis, 2018).

Questions to authors

1. Please use forward referencing in the introduction to point out where a contribution is detailed. Not just in the listed contributions, but also in the second-half of the first page (just before the contributions are listed). 2. Please clearly define what the plus/minus margins in the tables indicate. Are these standard errors? 3. In Table 4, I was wondering why there are no Duck + PF ablation results for k = 5 and k = 2. Could you please elaborate on this? 4. The font size in Fig. 1 is way too small. Please see if it's possible to increase this for readability.

Additional Updates

Here are some additional updates addressing your other points. 4. In general, if you feel there are any key works our findings reinforce and we should include, we would be happy to add discussion and references. Regarding the point about “no single source” in particular, we felt that it was important to verify that our approach works without searching PolitiFact. In some cases there are existing fact-check articles to draw on, but in other cases there are no such sources, and we sought to develop a method that worked in both scenarios. This type of analysis has been omitted in some literature like HiSS (open web) and the many papers that draw on Wikipedia (fixed and not necessarily comprehensive knowledge base). It is reasonable though that more sources should be helpful up to a point. Also, it may be that the reason using a stronger summarizer performs better (Table 5) is that it is better at reconciling conflicting sources like you highlighted; more broadly, this would be an exciting topic for future work. 5. The claims are generally atomic to the extent that they have verdicts from professional fact-checkers and are commonly evaluated with binary labels (e.g., true/false). In particular, LIAR-New is sourced in nearly the same way as the very widely used - and widely used with binary labels - LIAR dataset; it just includes more recent examples. Likewise, FEVER v2 is also commonly evaluated with binary labels. Therefore, our approach follows standard practices to facilitate comparison between methods. We found that although not explicitly instructed to do it, our method naturally leads the LLM to decompose its analysis through multiple steps of reasoning and multiple searches. For example, the interaction shown in Table 1 is evaluating the statement “Comparing the price of oil and gas in June 2008 to March 2022 shows that oil companies are ‘price gouging.’” We see in Table 1 how it searches for 2008, and then after that (not shown in Table) it searches for 2022. We will add some examples and clarify the writing in the paper. 6. We have now added class-wise precision and recall to the appendix. Please let us know if there are any particular cases you would like to see here. 7. We have adjusted the writing to include forward referencing, better font size in Fig. 1, and description of the plus/minus margins. The latter are 95% confidence intervals over 5 runs. Thank you again for your time, and as before would love to hear your thoughts on these changes or any other feedback you may have!