Knowledge Verification to Nip Hallucination in the Bud

• URL: http://arxiv.org/abs/2401.10768v5
• Date: Sun, 22 Sep 2024 03:32:52 GMT
• Title: Knowledge Verification to Nip Hallucination in the Bud
• Authors: Fanqi Wan, Xinting Huang, Leyang Cui, Xiaojun Quan, Wei Bi, Shuming Shi,
• Abstract summary: We demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs. We propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-aligned LLM to automatically formulate assessments based on external knowledge. We demonstrate the superior efficacy of KCA in reducing hallucinations across six benchmarks, utilizing foundation LLMs of varying backbones and scales.
• Score: 69.79051730580014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination. In this paper, we demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs. Specifically, we propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-aligned LLM to automatically formulate assessments based on external knowledge to evaluate the knowledge boundaries of foundation LLMs. To address knowledge inconsistencies in the alignment data, KCA implements several specific strategies to deal with these data instances. We demonstrate the superior efficacy of KCA in reducing hallucinations across six benchmarks, utilizing foundation LLMs of varying backbones and scales. This confirms the effectiveness of mitigating hallucinations by reducing knowledge inconsistency. Our code, model weights, and data are openly accessible at \url{https://github.com/fanqiwan/KCA}.

Related papers

• Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective [5.769786334333616]
  Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, and others. They face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This paper discusses these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations.
  arXiv  Detail & Related papers  (2024-11-21T16:09:05Z)
• LLM Hallucination Reasoning with Zero-shot Knowledge Test [10.306443936136425]
  We introduce a new task, Hallucination Reasoning, which classifies LLM-generated text into one of three categories: aligned, misaligned, and fabricated. Our experiments conducted on new datasets demonstrate the effectiveness of our method in hallucination reasoning.
  arXiv  Detail & Related papers  (2024-11-14T18:55:26Z)
• Knowledge Graph-Enhanced Large Language Models via Path Selection [58.228392005755026]
  Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. LLMs are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. We propose a principled framework KELP with three stages to handle the above problems.
  arXiv  Detail & Related papers  (2024-06-19T21:45:20Z)
• KnowTuning: Knowledge-aware Fine-tuning for Large Language Models [83.5849717262019]
  We propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs. KnowTuning generates more facts with less factual error rate under fine-grained facts evaluation.
  arXiv  Detail & Related papers  (2024-02-17T02:54:32Z)
• Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models [68.91592125175787]
  Hallucinations pose a significant challenge for the practical implementation of large language models (LLMs) We present Rowen, a novel approach that enhances LLMs with a selective retrieval augmentation process tailored to address hallucinations.
  arXiv  Detail & Related papers  (2024-02-16T11:55:40Z)
• Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation [9.730412606588335]
  We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state. We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs.
  arXiv  Detail & Related papers  (2024-01-27T16:19:30Z)
• Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting [51.7049140329611]
  This paper proposes Knowledge Graph-based Retrofitting (KGR) to mitigate factual hallucination during the reasoning process. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T11:08:38Z)
• Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus [99.33091772494751]
  Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations. We propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs.
  arXiv  Detail & Related papers  (2023-11-22T08:39:17Z)

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.