Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (2023)

Abstract: Claim verification plays a crucial role in combating misinformation. While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale. Additionally, it is important for models to provide comprehensive explanations that can justify their decisions and assist human fact-checkers. This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK) Reasoning that can verify complex claims and generate explanations without the need for annotated evidence using Large Language Models (LLMs). FOLK leverages the in-context learning ability of LLMs to translate the claim into a First-Order-Logic (FOL) clause consisting of predicates, each corresponding to a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning over a set of knowledge-grounded question-and-answer pairs to make veracity predictions and generate explanations to justify its decision-making process. This process makes our model highly explanatory, providing clear explanations of its reasoning process in human-readable form. Our experiment results indicate that FOLK outperforms strong baselines on three datasets encompassing various claim verification challenges. Our code and data are available.

## introduction claim verification @xcite has become increasingly important due to widespread online misinformation @xcite . most of the existing claim verification models @xcite @xcite @xcite use an automated pipeline that consists to this end, we propose a novel framework first-order-logic-guided knowledge-grounded (folk) to perform explainable claim verification by leveraging the reasoning capabilities of large language models (llms) @xcite @xcite . figure 1 illustrates a real-world example from folk where it can provide veracity prediction based on logical reasoning and generate an explanation of its decision-making process in a short paragraph. to ensure accurate prediction and provide high-quality explanations, folk first translates the input claim into a first-order-logic (fol) @xcite clause consisting of a set of conjunctive predicates. each predicate represents a part of the claim that needs to be verified. next, the generated fol predicates guide llms to generate a set of questions and corresponding answers. although the generated answers may appear coherent and plausible, they often lack factual accuracy due to llm's hallucination problem @xcite . to address this problem, folk controls the knowledge source of the llms by grounding the generated answers in real-world truth via retrieving accurate information from trustworthy external knowledge sources (e.g. google or wikipedia). finally, folk leverages the reasoning ability of llms to evaluate the boolean value of the fol clause and make a veracity prediction. given the high stakes involved in claim verification, folk prompts the llms to generate justifications for their decision-making process in natural language. these justifications are intended to aid human fact-checkers in making the final verdict, enhancing the transparency and interpretability of the model's predictions. we evaluate our proposed methods on three factchecking datasets @xcite @xcite with the following distinct challenges: multi-hop reasoning, numerical reasoning, combining text and table for reasoning, and open-domain scientific claim verification. our experiment results demonstrate that folk can verify complex claims while generating explanations to justify its decision-making process. additionally, we show the effectiveness of fol-guided claim decomposition and knowledge-grounded reasoning for claim verification. in summary, our contributions are: while steady progress has been made in this field, recent research focus has shifted to 1) dealing with insufficient evidence @xcite and 2) using explainable fact-checking models to support decision-making @xcite ). in the line of explainable fact-checking, @xcite and @xcite use visualization of neural attention weights as explanations. although attention-based explanation can provide insights into the deep learning model's decision process, it does not generate human-readable explanations and cannot be interpreted without any prior machine learning knowledge. @xcite formulate the task of generating explanations as extractive summarizing of the ruling comments provided by professional fact-checkers. while their work can generate high-quality explanations based on training data from professional fact-checkers, annotating such datasets is expensive and not feasible at a large scale. our work explores using reasoning steps as explanations of the model's decision-making process while generating explanations in natural language. large language models for reasoning. large language models have demonstrated strong reasoning abilities through chain-of-thought (cot) prompting, wherein llm is prompted to generate its answer following a step-by-step explanation by using just a few examples as prompts. recent works have shown that cot prompting can improve performance on reasoning-heavy tasks such as multi-hop question answering, multi-step computation, and common sense reasoning @xcite @xcite . verifying complex claims often requires multistep (multi-hop) reasoning @xcite , which requires combining information from multiple pieces of evidence to predict the veracity of a claim. multi-step reasoning can be categorized into forward-reasoning and backward-reasoning @xcite . forward-reasoning @xcite @xcite employs a bottom-up approach that starts with existing knowledge and obtains new knowledge with inference until the goal is met. backward-reasoning @xcite on the other hand, is goal-driven, which starts from the goal and breaks it down into sub-goals until all of them are solved. compared to forward reasoning, backward reasoning is more efficient, the divide-and-conquer search scheme can effectively reduce the problem search space. we propose folk, a fol-guided backward reasoning method for claim verification. despite the recent progress in using llms for reasoning tasks, their capability in verifying claims has not been extensively explored. @xcite evaluate using llms to generate reasoning traces and task-specific actions on fact verification tasks. their reasoning and action steps are more complex than simple cot and rely on prompting much larger models (palm-540b). additionally, they test their model's performance on the fever dataset @xcite , which lacks manyhop relations and specialized domain claims. in contrast to their approach, our proposed method demonstrates effectiveness on significantly smaller llms without requiring any training, and we test our method on scientific claims. contemporaneous to our work, @xcite propose a set of plug-and-play modules that augment with llms to improve the factuality of llm-generated responses for task-oriented dia-logue and question answering. in contrast to their approach, our primary focus is on providing llms with knowledge-grounded facts to enable fol-guided reasoning for claim verification, rather than solely concentrating on enhancing the factual accuracy of llms' responses. programfc @xcite leverages llms to generate computerprogram-like functions to guide the reasoning process. in contrast to their approach, which only uses llms for claim decomposition, we use llms for both claim decomposition and veracity prediction. by using llms for veracity prediction, we can not only obtain a comprehensive understanding of llms' decision process but also generate explanations for their predictions. furthermore, programfc is limited to closed-domain, as it needs to first retrieve evidence from a large textual corpus like wikipedia. folk on the other hand, can perform open-domain claim verification since it does not require a pre-defined evidence source. ## method our objective is to predict the veracity of a claim c without the need for annotated evidence while generating explanations to elucidate the decisionmaking process of llms. as shown in figure 1 , our framework contains three stages. in the fol-guided claim decomposition stage, we first translate the input claim into a fol clause p, then we use p to guide llm to generate a set of intermediate question-answer pairs (q i , a i ). each intermediate question q i represents a specific reasoning step required to verify the claim. in the knowledge-grounding stage, each a i represents the answer generated by llms that has been verified against ground truth obtained from an external knowledge source. finally, in the veracity prediction and explanation generation stage, we employ p to guide the reasoning process of llms over the knowledgegrounded question-and-answer pairs. this allows us to make veracity predictions and generate justifications for its underlying reasoning process. ## experiments we compare folk to existing methods on 7 claim verification challenges from three datasets. our experiment setting is described in sections 4.1 & 4.2 and we discuss our main results in section 4.4. ## conclusion in this paper, we propose a novel approach to tackle two major challenges in verifying real-world claims: the scarcity of annotated datasets and the absence of explanations. we introduce folk, a reasoning method that leverages first-order logic to guide llms in decomposing complex claims into sub-claims that can be easily verified through knowledge-grounded reasoning with llms. our experiment results show that folk demonstrates promising performance on three challenging datasets with only 4-6 in-context prompts provided and no additional training. additionally, we investigate the impact of knowledge grounding and model size on the performance of folk. the results indicate that folk can make accurate predictions and generate explanations when using a medium-sized llm such as llama-30b. to evaluate the quality of the explanations generated by folk, we conducted manual evaluations by three human annotators. the results of these evaluations demonstrate that folk consistently outperforms the baselines in terms of explanation overall quality.