Multi-hop Evidence Pursuit Meets the Web: Team Papelo at FEVER 2024 (2024)

Abstract: Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims. We integrate LLMs and search under a multi-hop evidence pursuit strategy. This strategy generates an initial question based on an input claim using a sequence to sequence model, searches and formulates an answer to the question, and iteratively generates follow-up questions to pursue the evidence that is missing using an LLM. We demonstrate our system on the FEVER 2024 (AVeriTeC) shared task. Compared to a strategy of generating all the questions at once, our method obtains .045 higher label accuracy and .155 higher AVeriTeC score (evaluating the adequacy of the evidence). Through ablations, we show the importance of various design choices, such as the question generation method, medium-sized context, reasoning with one document at a time, adding metadata, paraphrasing, reducing the problem to two classes, and reconsidering the final verdict. Our submitted system achieves .510 AVeriTeC score on the dev set and .477 AVeriTec score on the test set.

## introduction since 2018, the fever shared task has challenged natural language processing systems to verify claims using a corpus and provide evidence that witnesses these verdicts. it has evolved from a simple combination of natural language inference (nli) and entailment @xcite to a challenge involving adversarially constructed claims @xcite , to a challenge to verify complex, multi-hop claims using a combination of tables and free text @xcite . in the current task, it finally arrives at combating real-life disinformation on the web @xcite . systems are challenged to classify claim texts as supported, refuted, not enough evidence, or conflicting evidence/cherrypicking. in addition to classifying the claim, the systems must submit a list of questions and answers about a claim as evidence, with each answer derived from information on the open web and cited with a url. credit is given only when both the classification matches the ground truth and the evidence is adequate. the averitec score determines evidence adequacy by thresholding an average of meteor scores between each gold qa pair and the corresponding submitted qa pair in the best assignment of qa pairs. this task may involve retrieval and reasoning skills at a level for which professional journalists are sometimes employed. the reasoning may involve quote verification, stance detection, or numerical comparisons. the retrieval challenge goes beyond previous political fact-checking tasks @xcite and even beyond previous fever tasks in advancing from a closed corpus (wikipedia) to the open web. whereas previous fever shared tasks needed to be solved by researcher-trained models, the current shared task allows the use of commercial api components. the winning team in feverous based their retriever on fitting a dense passage retriever @xcite to the feverous data @xcite , but the training data for fever 2024 is quite limited, consisting of only 3,068 claims, and a retriever trained on user feedback from worldwide search queries should easily be more powerful. additionally, an external web search engine such as google search may provide additional query understanding features not found in dpr, as a recent feature (not in the api we used) applies generative ai to search foot_0 . even though the gold evidence documents are guaranteed to appear in the knowledge store provided by the contest organizers, the snippets may not be extracted successfully. we found that 297 of the 500 claims in the dev set included gold documents with empty extracted text. in contrast, web search provides at least some text even from pages that the provided web scraper is blocked from accessing. therefore, we chose to incorporate web search into our system. relying on a large language model (llm) such as gpt-4o (openai, 2024) for reasoning lets us leverage skills that could not be learned from 3,068 heterogenous claims, and go beyond the simple semantic comparison of an nli model. beyond simple nli, chatgpt and gpt-4 have been utilized to detect hallucinations in text summaries @xcite , as multi-faceted evaluators that score generated text @xcite , and for critiques and corrections of generated text @xcite . though there are many ways of using a search engine and llm within a fact-checking system, our main contribution is to show the power of combining them in a strategy of multi-hop evidence pursuit, which formulates additional questions only after searching and formulating answers to previous questions. in the following sections, we also investigate the impact of many choices of how the questions could be generated, the nature and size of context for generating answers, handling of multiple search results, metadata, paraphrasing, reducing the problem to two classes, and reconsidering the final verdict. ## related work retrieval-augmented generation (rag) @xcite provides a general paradigm for enabling an llm to answer questions that surpass the knowledge encoded in the llm parameters, which is a task somewhat isomorphic to verifying claims @xcite . a growing body of work utilizes llms as highlevel reasoning controllers that can solve tasks by querying agents to provide information or solve subproblems @xcite ). an early example for fact-checking an llm's own output was llm-augmenter @xcite , which called an open retrieval pipeline as an agent action to iteratively improve an llm response. @xcite uses an llm to rewrite, decompose, and disambiguate queries before searching, and these steps are made into a hierarchy of agents in @xcite . @xcite used a combination of google search and gpt-4 with a single hop to fact-check claims in the factool-kb, felm-wk, and halueval datasets. behind a closed api, searchgpt has been launched in beta to a few users as a service to provide access to a search-empowered openai llm. 2 fever 2024 presents a multi-hop, open corpus fact verification challenge. in the multi-hop shared task of feverous, all but two contestants collected all the needed evidence up front, after only reading the claim @xcite . later top performers (dcuf, unifee, see-st) addressed evidence interaction with graph-based methods but still did not address evidence that might be missed by the initial document retrieval @xcite @xcite . @xcite established an iterative paradigm for fact verification that retrieves further documents, sentences, and table cells by generating follow-up queries that are formulated after considering only the first retrieval, which we follow in the present system, in multi-hop evidence pursuit. in medical question answering, xiong et al. (2024) contemporaneously has proposed "iterative rag for medicine" which uses an llm to generate follow-up questions considering previous retrievals. in our algorithm, the relevance of each question is assured by generating it only upon a failure to verify the claim as true or false based on the existing evidence. their method may generate irrelevant questions after an answer could already be obtained, simply because the fixed numbers of questions are not achieved, resulting in lower evidence relevance and higher computational cost. our system can stop as soon as a verdict is clear, and if our system is configured to generate additional questions by paraphrasing, their relevance is assured by their similarity to the original questions. ## experiments we implement algorithm 1 using gpt-4o (gpt-4o-2024-05-13, seed 42) as the llm, t5 (t5-large) @xcite as the sequence-to-sequence model, and google as the web search engine, and consider various ablations. for a faster development cycle and reduced monetary cost, ## conclusion the averitec shared task is a realistic factchecking challenge on actual web disinformation. the best large language models offer the deep reasoning power needed to pursue missing evidence to verify claims, and the best web search engines provide the vast document indices and retrieval capabilities needed to find it. we have contributed a multi-hop evidence pursuit framework which combines the strengths of sequence to sequence models with llms to generate first question and subsequent questions separately, considering the present information; to stop pursuit once the answer is clear; and to embellish evidence by paraphrasing before considering the whole evidence chain to make the final verdict. ablations indicate the importance of each design choice. multi-hop evidence pursuit outperforms trying to generate all questions in one step. reducing the number of classes, and using metadata and multi-sentence context from one best document, were important in obtaining our best performance. the fact checking system presented may be useful to expedite the work of human fact checkers or provide a more rapid preliminary response to disinformation. its full explainability could mitigate the effect of misclassifications, if the explanations were read and considered by a human. over a history of many claims, ratings of disinformation from our system and/or human fact checkers could be used to rate the credibility of an information source.