Enhancing Perception: Refining Explanations of News Claims with LLM Conversations (2024)

Abstract: We introduce Enhancing Perception, a framework for Large Language Models (LLMs) designed to streamline the time-intensive task typically undertaken by professional fact-checkers of crafting explanations for fake news. This study investigates the effectiveness of enhancing LLM explanations through conversational refinement. We compare various questioner agents, including state-of-the-art LLMs like GPT-4, Claude 2, PaLM 2, and 193 American participants acting as human questioners. Based on the histories of these refinement conversations, we further generate comprehensive summary explanations. We evaluated the effectiveness of these initial, refined, and summary explanations across 40 news claims by involving 2,797 American participants, measuring their self-reported belief change regarding both real and fake claims after receiving the explanations. Our findings reveal that, in the context of fake news, explanations that have undergone conversational refinement—whether by GPT-4 or human questioners, who ask more diverse and detail-oriented questions—were significantly more effective than both the initial unrefined explanations and the summary explanations. Moreover, these refined explanations achieved a level of effectiveness comparable to that of expert-written explanations. The results highlight the potential of automatic explanation refinement by LLMs in debunking fake news claims.

## introduction misinformation has increasingly become a significant threat to contemporary society, undermining public trust and distorting democratic discourse. @xcite @xcite in recent years, fact-checking organizations such as * co-first author politifact, factcheck.org, and the washington post's fact checker have made significant impacts on the political landscape by holding public figures accountable for their statements @xcite . however, due to the absence of a reliable source that can reflect the most up-to-date information, such fact-checking cannot be conducted in a fully automated manner and thus requires human involvement @xcite . this necessity increases both the time and labor costs associated with debunking fake news. recently, the advent of large language models (llms), like @xcite , marks a significant milestone in the field of natural language processing (nlp). these models possess the capacity to generate coherent and contextually relevant explanations, thereby offering a promising base for simplifying the extensive and demanding tasks that fact-checkers usually undertake to identify and debunk fake news. previous research has demonstrated that gpt-generated summarizations of model selfrefinement conversations show improvement over base medical explanations across three clinicallyfocused tasks @xcite . despite this progress, the effectiveness of summarybased explanations in the context of debunking misinformation remains unexplored. moreover, while large language models (llms) have demonstrated exceptional capabilities in text generation and are widely accessible, their efficacy in generating explanations for debunking fake news, compared to expert-written content, is still under debate. in response to the challenges and unanswered questions posed by misinformation, our research aims to investigate the potential of llms to produce more persuasive and comprehensive explanations for debunking misinformation. this is achieved through the use of conversational self-refinement and summarization techniques. we have specifically designed our figure 1 : overview of enhancing perceptions: this two-phase framework begins with phase 1, where gpt-4 serves as an explainer, utilizing a provided evidence list and a verified label to produce an initial explanation to a claim. this explanation is then refined through iterative questioning by one of the various questioner agents, including gpt-4, claude, palm2, or humans. in phase 2, the detailed conversation history, claim, evidence list and label are then condensed into a summary explanation. in this study, we assess the effectiveness of various explanation settings using 40 claims verified by experts at politifact 1 , engaging a diverse range of questioner agents, including state-of-the-art llms and human participants. we explore how these agents can aid in the iterative refinement of explanations to enhance users' evaluation capabilities. the framework of our approach is depicted in figure 1 . by engaging these agents to refine explanations provided by the gpt-4 explainer for up to 15 iterations, we conduct a human-subject evaluation to assess the impact of different explanation settings. our assessment focuses on how these explanations influence users' perceptions of real and fake news claims and explores the differences among questioner agents by hand-coding their questions. given the diverse content that llms can produce due to their training, we hypothesize that the diversity and quality of 1 https://www.politifact.com/ questions significantly affect the outcomes of refinement. to enhance the understanding of the generated explanations, we incorporate an analysis using the linguistic inquiry and word count (liwc) tool(ryan l. boyd and pennebaker, 2022), aiming to provide deeper insights into the psycholinguistic patterns present. overall, our study is guided by the following research questions: ## methodology different from end-to-end explanation generation pipelines @xcite , our framework includes different questioner agents in each iteration in the explanation refinement process. we have established a two-phase framework for refining and summarizing explanations of news claims, as illustrated in figure 1 . initially, the gpt-4 explainer provides an initial explanation. subsequently, the questioner agent-represented by either gpt-4, claude 2, palm 2, or a human-evaluates this explanation across five distinct aspects. the questioner then poses questions focusing on one of the explanation's identified weakest aspect in each round. based on the received feedback, the gpt-4 explainer refines the explanation to enhance its clarity and persuasiveness. this iterative refinement process continues for at least 3 rounds, ending when the questioner decides to end the conversation or after a maximum of 15 rounds. at the conversation's end, the gpt-4 explainer produces a refined last round explanation. the gpt-4 summarizer then generates a summary explanation base on the entire conversation history of the explainer and the questioner. a detailed description of this twophase framework is provided below. ## experiment results we initially address rq1 of our study to focus on evaluating the effectiveness of refined last round explanations and summary explanations, produced after the refinement process, in comparison with expert-written explanations and initial explanations generated by the gpt-4 explainer. following this analysis, we proceed to address our rq2 and rq3 in section 6. we will discuss the impact of questions posed by different questioner agents during the refinement process. this exploration aims to understand how the diversity and quality of these questions contribute to the effectiveness of the refined explanations, providing further insights into the our explanation refinement framework. finally, we will explore the psycholinguistic patterns of explanations in different settings. refined explanations can be as effecitve as expert's explanations for fake claims figure 1 presents the findings from our human-subject evaluation. in the context of fake claims, we found the initial explanations were significantly worse than expert-written explanations. however, after the refinement process, both gpt-4 refined last round explanations 3 and human refined last round explanations 4 outperformed initial explanations, with chi-squared analysis confirming a substantial difference in effectiveness. furthermore, both gpt-4 and human refined last round explanations was comparable to that of expert-written explanations 5 . these results highlight the significant impact of both gpt-4 and human-refined explanations in correcting users' misconceptions about misinformation, marking a clear improvement over the gpt-4's initial explanations. however, claude 2's refined last round explanations, do not result different effectiveness from the initial explanations 6 and are significantly less effective than expert-written explanations 7 . this could be due to claude 2's higher rate of question duplication, as suggested by ## discussion we randomly selected 20 claims, evenly distributed between real and fake, to conduct hand-coded topic analysis. we further broke them down by the first three words of the questions asked by all questioner agents. the visualized results for real and fake claims can be found in figs. 4 and 5 , respectively. ## conclusion our investigation into the iterative refinement of explanations for both real and fake news claims reveals insights into the field of debunking misinformation. notably, gpt-4 and human questioners emerge as significantly effective in refining explanations, highlighting the power of diverse and in-depth questioning. this contrasted with the performances of palm 2 and claude 2, which did not exhibit noticeable improvement in explanation quality, underscoring the importance of the questioning approach's diversity and depth. our analysis further demonstrated that participants with initially neutral opinions were more amenable to changing their views, emphasizing the potential impact of well-crafted explanations on those undecided about a claim's veracity. however, the skepticism expressed by human questioners towards ai-generated explanations underscores the ongoing challenge of establishing ai credibility in misinformation mitigation efforts. moreover, our study introduced liwc-22 analysis to examine the linguistic characteristics of the explanations, revealing that the length and the linguistic features did not necessarily enhance effectiveness of explanations. this finding suggests that the effectiveness of expert explanations may derive from their ability to provide depth and context beyond what is captured by psycholinguistic patterns alone. this insight emphasizes the need for future misinformation explanations to go beyond surface linguistic patterns to have deeper engagement with the underlying context to truly influence the audience.