J usti LM : Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims (2024)

Abstract: Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation has been previously oversimplified as summarization of a fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim (for Explainable fact-checking of real-world Claims), and introduce JustiLM, a novel few-shot Justification generation based on retrieval-augmented Language Model by using fact-check articles as an auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.1 Code and dataset are released at https://github.com/znhy1024/JustiLM.

## introduction automated fact-checking typically encompasses several stages: identify check-worthy claims, retrieve relevant evidence, determine the claim's veracity using the retrieved evidence, and generate justification for the verdict on the veracity @xcite . despite a wealth of research focusing on the initial three stages, justification generation has remained under-explored in the past. justifications present essential evidence and rationales used to arrive at a claim's veracity judgment, serving to convince readers and enhance the credibility of fact-checking systems. this explanatory process is of paramount importance in gaining the user's trust in automated fact-checking @xcite . several methods have attempted to generate justification of verdict by summarizing fact-check articles that were previously authored by human fact-checkers @xcite @xcite . since a fact-check article per se is manually written to justify the verdict of a given claim with detailed presentation and reasoning over digested evidence, referring to reference documents collected from multiple sources, directly generating a summary from such a report as justification sidesteps the realistic challenges of evidence gathering and evidence-based reasoning for veracity assessment we essentially face in the fact-checking task. more importantly, these existing methods are impractical because fact-check articles are not available for new claims that are yet to check @xcite . in this paper, we propose a more realistic approach for the task of justification generation based on a language model approach, which complies with the process of journalistic fact-checking by well-known fact-check organizations such as politifact. 2 our goal is to produce high-quality justifications, drawing upon evidence gathered from diverse sources. to this end, we construct a benchmark dataset for explainable fact-checking of real-world claims, named exclaim, derived from a public dataset, watclaimcheck @xcite , containing newsworthy claims along with their fact-check articles and reference documents. exclaim provides a large searchable corpus by mixing the reference documents from all claims in watclaimcheck. additionally, it curates the verdict justifications sourced from fact-check articles, typically located in a conclusive paragraph marked by cue phrases like ''our ruling'' or ''our rating'' for each claim. furthermore, we develop a justification language model called justilm for generating the rationales behind veracity judgement within the context of few-shot learning. presumably, few-shot fine-tuning can mitigate the training resource requirements and its dependence on high-end hardware, often financially prohibitive, and also enables the model to achieve comparable effectiveness to state-of-the-art fullytrained models. justilm utilizes fact-check articles as auxiliary information in its training only via fine-tuning a pre-trained retrieval-augmented generation (rag) model on our curated justification dataset. meanwhile, leveraging fact-check articles for training enhances the model's proficiency in generating rationales based on evidence and articulating them in its generated content. our contributions are threefold: ## exclaim dataset the existing fact-checking datasets based on realworld claims have limitations for justification generation. this is because the provided evidence sources might not cover the evidence documents that fact-checkers actually rely on when writing justifications. for example, some datasets @xcite @xcite only provide metadata like speaker, party, and date without a sizeable knowledge corpus for finding specific evidence. some studies @xcite @xcite @xcite utilize web search to gather evidence documents, which result in retrieved information from non-authoritative sources or lead to the leak of ground truth by inadvertently including articles verifying the same claims by other organizations or sharing the fact-check information @xcite . more notably, certain studies @xcite @xcite @xcite regard fact-check articles as a primary source of evidence, a practice that may not align with realistic fact-checking procedures. we use the watclaimcheck @xcite ) dataset that provides the real-world claims along with the text of reference documents cited by fact-check articles. however, watclaimcheck is constructed for veracity classification and does not provide ground-truth justifications. for our task, we construct exclaim based on watclaimcheck, for which we additionally extract justifications from fact-check articles based on the cue phrases such as ''our ruling'' or ''our rating'' in the reports following previous works @xcite @xcite and remove the instances that do not have such justification content. after extracting the justifications, we also remove them from factcheck articles. ## methodology we base our approach on the retrieval-augmented generation (rag) framework @xcite @xcite , which contains a retriever for fine-grained evidence retrieval and a lm for textual justification generation. as shown in figure 1 , the retriever takes the claim text as input and retrieves the top-n chunk-level documents from the textual knowledge corpus, and the lm conditions on these documents together with the claim to generate justification. the retriever and lm can be jointly trained within a single rag framework, which makes it possible to utilize factcheck articles as an auxiliary resource to provide supervisory signals during training, targeting to enhance the quality of generated justification. we employ atlas @xcite as our backbone model considering two main reasons: 1) its strong few-shot learning ability in knowledge intensive tasks when its retriever and lm are jointly trained; 2) its flexibility for incorporating factcheck articles in the training process. ## discussion there is no passage-/sentence-level annotation in the original long-form reference documents and fact-check articles, which are costly to obtain. we do not have ground truths for training and evaluating evidence retrieval model. since these long documents bury specific evidence in them, directly using them for training will introduce a considerable amount of irrelevant text. while we mitigate this challenge by splitting each original reference document into disjoint 100-word chunks for retrieval, we believe that acquiring fine-grained evidence annotations will benefit the training and evaluation. in our experimental setup, evidence retrieval is conducted under the assumption that the needed evidence for fact-checking a given claim exists in the retrieval corpus. however, in a real-world searching scenario where gold evidence may be absent from the retrieval corpus, it is valuable to investigate how justification generation methods perform under this more challenging scenario by varying the ratio of gold reference documents in the retrieval corpus. additionally, while our experiments include the nli-based metric summacc, providing automated evaluation on the factuality of generated justifications, we believe that a sound human evaluation should involve professional fact-checkers. such evaluation, currently not conducted, necessitates close collaboration with fact-checking organizations and needs particular networking and setup, such as the integration with their existing workflow and the provision of motives for them to participate in evaluation, which could be warranted as a separate study by itself and is part of our future plan. as the sota llm, gpt-4 shows strong ability in generating factually consistent and informative justifications, therefore, developing justification methods based on those powerful api-based llms is beneficial. however, these blackbox llms have strict constraints on accessing their specific internal information, which poses important open challenges for being interacted with deeply and providing supervision signals to retriever. in this work, we address the justification generation task with a realistic approach, which generates justifications based on the retrieved evidence using an end-to-end retrieval-augmented language model. furthermore, incorporating our distillation techniques with the rag model atlas demonstrates a marked improvement in performance. this affirms that utilizing fact-check articles during training to provide supervision signals can strongly enhance justification generation. ## conclusion and future work we propose a justification generation language model justilm for realistic fact-checking of real-world news claims, where justification generation is performed based on retrieved evidence from large textual corpus, and introduce a new benchmark dataset exclaim for this task. justilm leverages fact-check articles as auxiliary resources during training to distill article-level and chunk-level training signals to guide justification writing. experimental results show justilm outperforms icl-enabled flan-t5 and llama2, as well as the sota few-shot rag model atlas. justilm also demonstrates comparable and promising performance when compared to gpt-4. in the future, we will explore the adaptation of various llm-based reasoning methods (e.g., cot @xcite , tot @xcite , and got @xcite ) into justilm to enhance the reasoning ability for improving the task of justification generation, which aims to assist the lms in providing better signals for guiding evidence retrieval and improving reasoning over retrieved evidence during justification generation. we also plan to develop a human evaluation scheme involving fact-checking experts to provide a more comprehensive and efficient assessment on machine-generated justifications.