I n F act: A Strong Baseline for Automated Fact-Checking (2024)

Abstract: The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems. The AVeriTeC Shared Task Challenge 2024 offers a realistic benchmark for text-based fact-checking methods. This paper presents Information-Retrieving Fact-Checker (InFact), an LLM-based approach that breaks down the task of claim verification into a 6-stage process, including evidence retrieval. When using GPT-4o as the backbone, InFact achieves an AVeriTeC score of 63% on the test set, outperforming all other 20 teams competing in the challenge, and establishing a new strong baseline for future text-only AFC systems. Qualitative analysis of mislabeled instances reveals that InFact often yields a more accurate conclusion than AVeriTeC’s human-annotated ground truth.

## introduction the weaponization of disinformation poses a critical threat to global stability. the world economic forum, in its january report @xcite , identified mis-and disinformation as the most significant global risk for the next 24 months, surpassing even extreme weather events and military conflicts. as such, the development and deployment of automated fact-checking (afc) is essential in safeguarding the integrity of democratic societies worldwide. @xcite introduced the automated verification of textual claims (averitec) benchmark, consisting of 4, 568 realworld claims subject to fact-checks by 50 organizations. averitec classifies each claim as either supported, refuted, nei (not enough information) or c/cp if there is conflicting evidence or the claim is technically true but misleading due to the exclusion of important * these authors contributed equally to this work. we propose information-augmented fact-checker (infact), an afc system with the capability of retrieving evidence. infact achieves an averitec score of 62.6% on the test set and yields an accuracy of 72.4% on the development dataset. qualitative analysis shows that our method's retrieval process and reasoning capabilities provide a powerful baseline for textonly afc. further details will be provided on https://github.com/multimodal-ai-lab/infact . ## the infact system open-domain, text-only claim verification requires world and commonsense knowledge and some degree of reasoning. due to their remarkable success in both of these skills, we chose to drive the factcheck by an llm, supplemented with a custom evidence retrieval module. while our approach is agnostic to the choice of the llm, the llm's abilities influence the quality and accuracy of the resulting fact-check. since the task of fact-checking in short, infact addresses the task with a static, single-pass pipeline that poses critical questions which are answered through evidence retrieved from the averitec kb. each of the six stages corresponds to an engineered prompt, applying prompting best practices including chain-of-thought @xcite and in-context learning @xcite , whenever applicable. stage 1: interpret the claim. the pipeline begins with an augmentation of the claim text with its author, date, and origin url. subsequently the llm is prompted to reformulate the claim, considering the supplied metadata. this step is helpful when the time frame is unclear as in "joe biden's income has increased recently." we also expect the interpretation to help when the claim misses context as in "tourism, lockdown key to deep new zealand recession." stage 2: pose questions. next, infact produces a list of 10 questions that it deems essential for fact-checking. to facilitate the question generation, we provide the llm with manually selected incontext examples. furthermore, the instructions are inspired by fact-checking best practices from @xcite . the averitec kb contains a collection of about 1, 000 resources per claim. a resource is a scraped url, ranging from news articles over social media posts to pdf documents. we decided to use the averitec kb over open-web search for two main reasons: first, it guarantees to contain the gold evidence (possibly erased from the open web) and, second, it yields reproducible results (in contrast to open-web search). to retrieve the most relevant resources from the kb, we implement a semantic search mechanism. for each resource, we compute its documentlevel embedding by employing a text embedding model. we chose gte-base-en-v1.5 (alibaba-nlp, 2024) due to its competitive fever score at time of the challenge given its manageable size. we compute the query's text embedding and use it to perform k-nearest neighbor search w.r.t. the euclidean distance in the document embedding space. this outputs a list of the semantically closest 5 resources. we drop resources that were found in previous searches and end up with a list @xmath1. we found this approach qualitatively superior to the common bm25 ranking method. stage 4: answer questions. taking all the search results, infact iterates from the semantically most similar to the least similar, instructing the llm to either answer the question using the information from the result or respond with none if the result is deemed unhelpful. if the llm returned an answer to the question, infact saves the answer along with the evidence url, and the q&a process continues with the next question. however, if the llm returned none for all search results, the question is dropped for the remainder of the fact-check. the llm takes the claim, all the qa pairs, the verdict, and any in-between reasoning (e.g., from stage 5) and creates a summary, focusing on the main reasons for the verdict. this stage is not required by the averitec task and does not affect any of the metrics. ## experimental results experimental setup. we evaluate infact on the development set which consists of 305 refuted, 122 supported, 35 nei and 38 c/cp claims, 500 claims in total. as our llm backbone, we test three models: (a) the open-source llama 3 (70b), (b) the closedsource gpt-4o mini, and (c) the more expensive gpt-4o model. we use the models without any finetuning and set the temperature to 0.01 and topp to 0.9. additionally, we truncate each resource to about 8 k tokens, which is the maximum input length of the embedding model. we compare in-fact with the following baseline and ablations: the naive baseline instructs the llm to predict the verdict right away in a single prompt, skipping evidence retrieval entirely and relying solely on the llm's parametric knowledge; the no interpretation ablation omits stage 1; no evidence answers the questions by leaving out stage 3 (evidence retrieval); no q&a generates search queries based on the claim instead of a q&a, gathers 10 results and proceeds to make a verdict based on those; no query generation skips the step of query generation by using the question as the search query directly. challenge results. surprisingly, in the naive setting, llama 3 outperforms the gpt models by a large margin. as opposed to the gpt models, llama 3 commits more often to either supported or refuted rather than choosing nei, showing a "self-confident" behavior despite having little evidence. in the no evidence variant, the gpt models achieve a higher accuracy and predict nei much less, while still having no access to any external information. this indicates that structured reasoning elevates gpt models' confidence, regardless of the knowledge source. qualitative analysis of 20 failure cases reveals that, in more than half of the cases, the ground truth was at least debatable or infact delivered a valid alternative fact-check. e.g., the ground truth of "while serving as town supervisor on grand island, nebraska, us nate mcmurray voted to raise taxes on homeowners" is supported, however mcmurray served on grand island, new york. in two cases, the gold fact-check considered a different claim than the one presented. e.g., the claim: "scientific american magazine warned that 5g technology is not safe" is about the magazine issuing a warning about 5g. however, the gold fact-check analyzed the safety of 5g itself. in only 6 of the analyzed 20 failure cases, the cause for the mislabeling can be clearly attributed to infact. the cases include the usage of unreliable evidence sources, misinterpretations of the claim, the missing ability to process non-textual evidence, and the confusion between clearly refuted and merely unsupported claims. in a nutshell, the analysis implies that the model performs better than the metrics might reflect. ## discussion & conclusion infact establishes a robust baseline for information-augmented fact-checking without requiring fine-tuning. its llm-agnostic design ensures that it benefits from advancements in the reasoning capabilities of llms, making it adaptable to future developments. additionally, infact provides justifications, enhancing interpretability and trust in its outputs. however, infact also has limitations. the inclusion of closed-source models limits transparency, reproducibility, and incurs high cost with about @xmath0 0.01 per claim), it exhibited lower performance. the open-source alternative llama 3 resulted in 8 times longer computation times and reduced effectiveness. also the number of retrievals was relatively high (7 searches per claim). increasing infact's efficiency by reducing searches and skipping and/or combining steps in the pipeline are a great opportunity for future work. all llms evaluated in this study were pre-trained on datasets that extend into 2023, likely covering many of averitec's claims and evidence available online. moreover, the averitec dataset comes with its own limitations. the wording of the qa pairs is crucial when using the meteor score to evaluate them against gold-standard qa pairs. the automated comparison method is limited in capturing semantically similar statements, and it is infeasible to provide an exhaustive list of all potentially relevant evidence. moreover, we found many questionable ground truth answers, cf. section 3. we suspect that these inaccuracies stem from layperson annotations. addressing these limitations and refining the dataset/metric will benefit measuring progress in this challenging task.