FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models

• URL: http://arxiv.org/abs/2502.18573v1
• Date: Tue, 25 Feb 2025 19:01:48 GMT
• Title: FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models
• Authors: Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran Tchrakian, Javier Carnerero Cano, Yufang Hou, Elizabeth Daly, Alessandra Pascale,
• Abstract summary: We propose FactReasoner, a new factuality assessor that relies on probabilistic reasoning to assess the factuality of a long-form generated response.<n>Our experiments on labeled and unlabeled benchmark datasets demonstrate clearly that FactReasoner improves considerably over state-of-the-art prompt-based approaches.
• Score: 59.171510592986735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated vast capabilities on generative tasks in recent years, yet they struggle with guaranteeing the factual correctness of the generated content. This makes these models unreliable in realistic situations where factually accurate responses are expected. In this paper, we propose FactReasoner, a new factuality assessor that relies on probabilistic reasoning to assess the factuality of a long-form generated response. Specifically, FactReasoner decomposes the response into atomic units, retrieves relevant contexts for them from an external knowledge source, and constructs a joint probability distribution over the atoms and contexts using probabilistic encodings of the logical relationships (entailment, contradiction) between the textual utterances corresponding to the atoms and contexts. FactReasoner then computes the posterior probability of whether atomic units in the response are supported by the retrieved contexts. Our experiments on labeled and unlabeled benchmark datasets demonstrate clearly that FactReasoner improves considerably over state-of-the-art prompt-based approaches in terms of both factual precision and recall.

Related papers

• Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs [12.489784979345654]
  We show that current versions of large language models (LLMs) lack the ability to provide rational and coherent representations of probabilistic beliefs. We apply well-established techniques for uncertainty quantification to measure the ability of LLM's to adhere to fundamental properties of probabilistic reasoning.
  arXiv  Detail & Related papers  (2025-04-18T11:50:30Z)
• Atomic Fact Decomposition Helps Attributed Question Answering [30.75332718824254]
  Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a question. This paper proposes an Atomic fact decomposition-based Retrieval and Editing framework. It decomposes the generated long-form answers into molecular clauses and atomic facts by the instruction-tuned LLMs.
  arXiv  Detail & Related papers  (2024-10-22T05:25:54Z)
• QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios [15.193544498311603]
  We present QUITE, a dataset of real-world Bayesian reasoning scenarios with categorical random variables and complex relationships. We conduct an extensive set of experiments, finding that logic-based models outperform out-of-the-box large language models on all reasoning types. Our results provide evidence that neuro-symbolic models are a promising direction for improving complex reasoning.
  arXiv  Detail & Related papers  (2024-10-14T12:44:59Z)
• Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic [51.967603572656266]
  We introduce a consistent and theoretically grounded approach to annotating decompositional entailment. We find that our new dataset, RDTE, has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality.
  arXiv  Detail & Related papers  (2024-02-22T18:55:17Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)
• Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval [139.21955930418815]
  Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts. We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
  arXiv  Detail & Related papers  (2023-09-29T09:41:19Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Reliable Post hoc Explanations: Modeling Uncertainty in Explainability [44.9824285459365]
  Black box explanations are increasingly being employed to establish model credibility in high-stakes settings. prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability. We develop a novel Bayesian framework for generating local explanations along with their associated uncertainty.
  arXiv  Detail & Related papers  (2020-08-11T22:52:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.