FACTS & EVIDENCE : An Interactive Tool for Transparent Fine-Grained Factual Verification of Machine-Generated Text (2025)

Abstract: With the widespread consumption of AI-generated content, there has been an increased focus on developing automated tools to verify the factual accuracy of such content. However, prior research and tools developed for fact verification treat it as a binary classification or a linear regression problem. Although this is a useful mechanism as part of automatic guardrails in systems, we argue that such tools lack transparency in the prediction reasoning and diversity in source evidence to provide a trustworthy user experience.We develop FACTS&EVIDENCE—an interactive and transparent tool for user-driven verification of complex text. The tool facilitates the intricate decision-making involved in fact-verification, presenting its users a breakdown of complex input texts to visualize the credibility of individual claims along with explanation of model decisions and attribution to multiple, diverse evidence sources. FACTS&EVIDENCE aims to empower consumers of machine-generated text and give them agency to understand, verify, selectively trust and use such text.

## introduction in our time of proliferating use and adoption of aigenerated text and ai-assisted writing, the need for robust and transparent fact verification tools to help users verify the accuracy of machine-generated content has never been more pressing. errors and hallucinations generated by ai models are already translating into real-world problems through errors in generated code introducing unintended bugs @xcite , nonexistent books and references propagating misinformation @xcite @xcite , and incorrect medical advice causing health complications (alstin, 2023), which cost stakeholders time, money, and in some cases leading to physical harm @xcite . although factverification of machine-generated texts has been studied extensively in recent research @xcite @xcite , this work has not resulted in practical tools that are accessible to users, easy to use, and transparent @xcite . as such, developing fact-verification tools aimed at informing users of the veracity of ai-generated content is necessary to enable users to understand, verify, and trust such content. recent research has developed methods for automatically detecting factual errors and hallucinations through self-checking with large language models (llms) @xcite , uncertainty calibration @xcite @xcite or fine-tuning specific factuality classifiers @xcite , and tools for visualizing such errors for users @xcite . the primary intent of this research has been the binary classification (factual vs. non-factual) of complex text to serve as an intrinsic guardrail in ai systems. as a result, their decisions are categorical, often not explainable, and can be noisy @xcite . on the other hand, real-world human factchecking is significantly more complex, involving verification against diverse sources with varying levels of reliability and leanings @xcite @xcite . evidence for verifying facts is traced through various sources, and claims are verified against each source. the authenticity and reliability of each source or news outlet is considered when aggregating and deciding the veracity of each claim. such fine-grained views into each claim per source provide a detailed understanding of the correctness of long, complex texts, allowing consumers of the text to assess which specific claims to trust and use. in this work, we take a step towards addressing this gap. we present facts&evidence-a userdriven, transparent and interactive fact-verification tool to empower consumers of ai-generated text to understand the factual accuracy of the text. facts&evidence takes any long, complex text from a user as input and presents a breakdown of the text into individual claims. each claim is searched on the web to identify multiple, diverse evidence items and verify against each of them. ultimately, decisions from multiple pieces of evidence and claims are aggregated to compute a credibility score for the entire text. in addition to aggregate decisions, facts&evidence presents the per-claim, per-evidence decision, along with a model-generated rationale explaining the decision and a source tag indicating the source category of the evidence. further, facts&evidence is interactive, allowing users to include or exclude any source type from the verification process based on personal preferences and specific needs. we evaluate the reliability of decisions produced by facts&evidence using the fava dataset @xcite and show that the predicted scores are competitive with strong baselines, outperforming previous systems by ∼ 40 f1 points, and establishing a high-quality system in claim verification of open-ended generation tasks. in summary, our contribution is facts&evidencea high-quality, fine-grained, and interactive tool providing transparency and access to real-time information in the fact-verification process which can inform and empower users, giving them the ability to inspect and validate the veracity of each part of the content they use. foot_0 ## facts&evidence -user interface this section presents the user interface of facts&evidence when it is used to inspect the veracity of machine-generated texts. once the text is provided and the user is satisfied with the parameters, they can submit their request to verify the text. readme: the home screen also includes a readme that explains the objectives of the tool and a brief explanation of how the system conducts fact verification behind the scenes to provide full transparency to users. it describes how a user can use the tool and what level of interaction they can expect from the tool. in addition, it includes an explanation for each configurable parameter and possible choices available to the user. ## facts&evidence -behind the scenes this section details the back-end framework for verifying facts in model-generated texts. for providing a detailed view of facts, evidence items, and their factual accuracy, the user text is broken down to individual claims, and for each claim, multiple, diverse supporting evidence items are retrieved from the web. a factual accuracy prediction and explanation for each claim is produced using an llm and the resulting details are displayed to the user as outlined in §2. figure 1 presents an outline of our process which we describe in detail below. ## experiments evaluation: we evaluate our predictions using the favabench dataset @xcite . favabench includes human factuality annotations at the span level for approximately 1,000 model-generated responses from two popular llms (chatgpt and llama2-chat) for 200 diverse information-seeking and knowledgeintensive queries. for chatgpt and llama2-chat, 59.8% and 65.5% of the respective responses include at least one hallucination, respectively. we obtain sentence-level labels by associating any sentence with a factually incorrect span with an incorrect sentence label. we run the favabench inputs ## related work factuality and hallucination in llms: recently, a focus has been on addressing the problem of model hallucinations @xcite @xcite . previous research has studied the variety of errors and hallucinations produced by different models and characterized them using taxonomies for summarization @xcite ), text simplification @xcite , dialogue generation @xcite , and open-ended generation @xcite . automated methods were developed to identify and flag model errors and hallucinations @xcite @xcite . although hallucination detection methods are primarily used as safety guardrails in systems, early tools have been developed to detect and present users with factual errors in model-generated text @xcite . while these tools primarily present single evidence and final model decisions, facts&evidence provides a transparent and fine-grained view of the factual accuracy of model-generated text with an interactive interface for consumers with diverse evidences, detailed credibility scores, and explanations. fact-verification: in parallel, there has been active research on automated fact verification of human written text and claims as well @xcite @xcite . various methods, tools, and systems have been built for automated fact checking of human-written claims @xcite . however, these techniques and systems have not been tested for lm-generated text. although the aim of facts&evidence is to support the verification and understanding of facts in the text generated by a model and has been explicitly evaluated for this setting, it can be directly adapted and used to verify written human claims. ## conclusion we presented facts&evidence-an interactive tool for user-driven fact verification of aigenerated text. the tool enables consumers of outputs from various llms to visualize and understand individual claims and ground the claims in diverse evidence from the web. we evaluate the decisions made by the facts&evidence system and show that our system outperforms strong baselines in the claim verification task. facts&evidence promotes transparency and user agency in the verification process of facts and aims to empower con-sumers to understand fine-grained claims and appropriately use text generated by ai systems.