F act R easoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models (2025)

Abstract: Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs thus limiting their reliability in real-world applications where correctness is critical. In this paper, we present FactReasoner, a novel neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses. FactReasoner decomposes a response into atomic units, retrieves relevant contextual information from external knowledge sources, and models the logical relationships (e.g., entailment, contradiction) between these units and their contexts using probabilistic encodings. It then estimates the posterior probability that each atomic unit is supported by the retrieved evidence. Our experiments on both labeled and unlabeled benchmark datasets demonstrate that FactReasoner often outperforms state-of-the-art prompt-based methods in terms of factual precision and recall.

## introduction large language models (llms) have achieved impressive improvements and demonstrated vast capabilities in recent years @xcite , however they still struggle to guarantee the factual accuracy of the generated content. specifically, llms often hallucinate, namely they produce factual errors in which a claim contradicts well-established ground-truth knowledge @xcite @xcite . this makes the models unreliable in realistic situations that require factually accurate llm-generated responses @xcite . most modern approaches for assessing the factuality of llm-generated long-form responses such as factscore @xcite , veriscore @xcite and others @xcite are prompt-based approaches and consist of three main stages: 1) the response is decomposed into a set of atomic units (facts or claims) which are subsequently revised or decontextualized to make them self-contained; 2) relevant evidence (or context) is retrieved for each atomic unit from an external knowledge source such as wikipedia, and 3) each atomic unit is evaluated against the retrieved context to determine whether it is supported (factually correct) or not and a factuality score is calculated for the response. these approaches often struggle due to conflicting information between the model's internal knowledge and conflicting information within the retrieved contexts themselves @xcite . in this paper, we introduce a new perspective on long-form factuality assessment that moves beyond traditional prompt-based approaches, particularly during the evaluation phase. we propose a novel factuality assessor, factreasoner, which decomposes a response into atomic units and retrieves relevant contextual evidence for each atom from an external knowledge source. unlike prior methods that rely on prompting a language model to evaluate these atoms against the retrieved evidence, factreasoner estimates the probability of each atom being supported by reasoning over a graphical model. this model encodes a joint probability distribution over the atoms and their associated contexts, constructed using probabilistic representations of entailment and contradiction relationships between the natural language utterances of the atoms and the retrieved contexts. factreasoner addresses three important limitations of the existing prompt-based approaches: context relevance across atoms in multi-atom responses, contexts retrieved for one atom can be relevant -either supportive or contradictory -to another atom. prompt-based methods struggle with this, as they require saturating the model's con-text window with all of the retrieved information. factreasoner overcomes this limitation using a compact probabilistic representation (i.e., a graphical model) of the relationships between all atoms in the response and all of the retrieved contexts. handling conflicting contexts sometimes, contexts retrieved for different atoms may contradict each other. factreasoner can leverage these contradictions effectively and in a principled manner by reasoning over their probabilistic encodings which often leads to improved performance. ## background we begin by providing background on graphical models and long-form factuality for llms. ## the factreasoner assessor in this section, we present factreasoner, a novel long-form factuality assessor that leverages probabilistic reasoning to assess the factuality of the generated response with respect to an external knowledge source c. specifically, factrea-soner constructs a graphical model that captures a joint probability distribution over the atomic units in the response and their corresponding contexts in c. for each atom a i , it then computes the posterior marginal probability distribution p (a i ), which quantifies the likelihood that a i is true (or supported) given the information available in c. ## experiments we empirically evaluate the factreasoner assessor for long-form factuality and compare it against state-of-the-art approaches on labeled and unlabeled datasets. although the factreasoner pipeline stages can be instantiated with different llms, in our implementation we use the same llm throughout the entire pipeline and focus our empirical evaluation on the evaluator stage (i.e., factuality assessment). furthermore, our opensource python implementation is publicly available at: https://github.com/ibm/factreasoner . baseline assessors. for our purpose, we consider the following state-of-the-art prompt-based long-form factuality assessors: factscore (fs) @xcite , factverify (fv) @xcite and veriscore (vs) @xcite . factscore is one of the first assessor that prompts an llm to assess whether an atomic unit of the response is supported or not by a set of contexts relevant to the atom which are retrieved from an external knowledge source such as wikipedia. factverify and veriscore are more recent refinements of factscore's original prompt that can accommodate other external knowledge sources such as google search results and enable the llm's reasoning capabilities to evaluate the relationships between an atom and its relevant contexts. unlike factscore, the latter can label the atoms as supported, contradicted and undecided, respectively. in our ex-periments, we instantiated the competing assessors including the factreasoner variants with opensource llms belonging to the ibm granite 3 , meta llama 4 and mistralai mixtral 5 families, namely: granite-3.0-8b-instruct, llama-3.1-70b-instruct, and mixtral-8x22b-instruct, respectively. all our llms are hosted remotely on compute nodes with a100 80gb gpus and accessed via litellm apis capable of serving 1500 prompts per second. datasets. we experimented with the following datasets: biographies (bio) @xcite , askhistorians (askh) @xcite , eli5 @xcite , freshbooks (books) @xcite , and longfact-objects (lfobj) @xcite . these datasets have been widely adopted in prior work and are considered representative benchmarks for long-form factuality assessment, as they encompass a diverse range of topics and tasks, including creative writing, history, astronomy, chemistry, and more. the biographies is the only labeled dataset available. it contains 157 biographies generated by chatgpt for various person entities that have a wikipedia page. each biographic passage is also associated with a set of human generated atomic units (facts) that were labeled as supported (s) or not-supported (ns) by human annotators. we assume that this annotation is the ground truth. the askh, eli5, books and lfobj datasets are unlabeled and consist of collections of prompts. specifically, the askh and eli5 datasets each contain 200 questions sourced from the reddit forums r/askhistorians and r/explainlikeimfive, respectively. the books dataset comprises 200 paragraphs, sampled as 10 excerpts from each of 20 non-fiction books published between 2023 and 2024. our version of the lfobj dataset is a curated subset of the original collection @xcite , consisting of 10 prompts randomly selected from those related to objects spanning 38 distinct topics. for each prompt across these datasets, we generated a long-form response -up to two paragraphs in length -using the llama-3.3-70b-instruct model @xcite . additionally, we constructed a new dataset, conflicts, comprising 1,000 claims (or atomic units) randomly sampled from the recent conflictbank benchmark @xcite nally extracted from wikidata, is considered true (i.e., supported). for every claim, we include two associated contexts: one supporting context (the default in conflictbank) and one conflicting context (representing misinformation, also provided by conflictbank). notably, these two contexts are mutually contradictory, thus offering a controlled setting for our long-form factuality evaluation. ## related work factuality evaluation of llms has received growing attention due to their widespread use. early benchmarks such as truthfulqa @xcite , freshqa @xcite , halueval @xcite , halluqa @xcite , and felm @xcite focus on short-form factuality, assessing isolated factoids. more recent work @xcite @xcite extends this to long-form responses by decomposing them into atomic facts evaluated against external evidence -typically assuming non-conflicting sources. however, conflicting information is common in real-world knowledge bases @xcite , posing challenges for retrieval-augmented generation systems @xcite . new benchmarks have emerged to capture such conflicts more realistically @xcite @xcite . our work is also related to recent efforts on improving self-consistency in llms through formal reasoning @xcite @xcite . ## conclusion this paper introduces a new approach to long-form factuality assessment through factreasoner, a novel assessor that leverages probabilistic reasoning to evaluate the factual accuracy of llmgenerated responses. like existing prompt-based methods, factreasoner decomposes responses into atomic units and retrieves relevant contexts from an external knowledge source. however, it goes further by modeling the logical relationships between atoms and contexts using a graphical model, enabling more robust factuality judgments. experiments on both labeled and unlabeled benchmarks show that factreasoner significantly outperforms existing prompt-based approaches.