Mitigating Entity-Level Hallucination in Large Language Models
- URL: http://arxiv.org/abs/2407.09417v2
- Date: Mon, 22 Jul 2024 12:28:05 GMT
- Title: Mitigating Entity-Level Hallucination in Large Language Models
- Authors: Weihang Su, Yichen Tang, Qingyao Ai, Changyue Wang, Zhijing Wu, Yiqun Liu,
- Abstract summary: This paper proposes Dynamic Retrieval Augmentation based on hallucination Detection (DRAD) as a novel method to detect and mitigate hallucinations in Large Language Models (LLMs)
Experiment results show that DRAD demonstrates superior performance in both detecting and mitigating hallucinations in LLMs.
- Score: 11.872916697604278
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions with LLMs. However, the widespread adoption of LLMs has revealed a significant challenge known as hallucination, wherein LLMs generate coherent yet factually inaccurate responses. This hallucination phenomenon has led to users' distrust in information retrieval systems based on LLMs. To tackle this challenge, this paper proposes Dynamic Retrieval Augmentation based on hallucination Detection (DRAD) as a novel method to detect and mitigate hallucinations in LLMs. DRAD improves upon traditional retrieval augmentation by dynamically adapting the retrieval process based on real-time hallucination detection. It features two main components: Real-time Hallucination Detection (RHD) for identifying potential hallucinations without external models, and Self-correction based on External Knowledge (SEK) for correcting these errors using external knowledge. Experiment results show that DRAD demonstrates superior performance in both detecting and mitigating hallucinations in LLMs. All of our code and data are open-sourced at https://github.com/oneal2000/EntityHallucination.
Related papers
- Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models [70.19081534515371]
Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks.
They generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences.
We propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers.
arXiv Detail & Related papers (2024-07-04T18:47:42Z) - Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [48.065569871444275]
We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback.
We generate a small-size hallucination annotation dataset by proprietary models.
Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
arXiv Detail & Related papers (2024-04-22T14:46:10Z) - Exploring and Evaluating Hallucinations in LLM-Powered Code Generation [14.438161741833687]
Large Language Models (LLMs) produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misalign with factual knowledge.
Existing work mainly focuses on investing the hallucination in the domain of natural language generation.
We conduct a thematic analysis of the LLM-generated code to summarize and categorize the hallucinations present in it.
We propose HalluCode, a benchmark for evaluating the performance of code LLMs in recognizing hallucinations.
arXiv Detail & Related papers (2024-04-01T07:31:45Z) - Retrieve Only When It Needs: Adaptive Retrieval Augmentation for
Hallucination Mitigation in Large Language Models [73.93616728895401]
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) - HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data [102.56792377624927]
hallucinations inherent in machine-generated data remain under-explored.
We present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm.
Our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA.
arXiv Detail & Related papers (2023-11-22T04:52:58Z) - AutoHall: Automated Hallucination Dataset Generation for Large Language Models [56.92068213969036]
This paper introduces a method for automatically constructing model-specific hallucination datasets based on existing fact-checking datasets called AutoHall.
We also propose a zero-resource and black-box hallucination detection method based on self-contradiction.
arXiv Detail & Related papers (2023-09-30T05:20:02Z) - Siren's Song in the AI Ocean: A Survey on Hallucination in Large
Language Models [116.01843550398183]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks.
LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
arXiv Detail & Related papers (2023-09-03T16:56:48Z)
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.