Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking

• URL: http://arxiv.org/abs/2501.01306v2
• Date: Fri, 03 Jan 2025 08:29:37 GMT
• Title: Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
• Authors: Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen,
• Abstract summary: HaluSearch is a novel framework that incorporates tree search-based algorithms. It frames text generation as a step-by-step reasoning process. We introduce a hierarchical thinking system switch mechanism inspired by the dual process theory in cognitive science.
• Score: 124.69672273754144
• License:
• Abstract: Large language models (LLMs) demonstrate exceptional capabilities, yet still face the hallucination issue. Typical text generation approaches adopt an auto-regressive generation without deliberate reasoning, which often results in untrustworthy and factually inaccurate responses. In this paper, we propose HaluSearch, a novel framework that incorporates tree search-based algorithms (e.g. MCTS) to enable an explicit slow thinking generation process for mitigating hallucinations of LLMs during inference. Specifically, HaluSearch frames text generation as a step-by-step reasoning process, using a self-evaluation reward model to score each generation step and guide the tree search towards the most reliable generation pathway for fully exploiting the internal knowledge of LLMs. To balance efficiency and quality, we introduce a hierarchical thinking system switch mechanism inspired by the dual process theory in cognitive science, which dynamically alternates between fast and slow thinking modes at both the instance and step levels, adapting to the complexity of questions and reasoning states. We conduct extensive experiments on both English and Chinese datasets and the results show that our approach significantly outperforms baseline approaches.

Related papers

• Think&Cite: Improving Attributed Text Generation with Self-Guided Tree Search and Progress Reward Modeling [63.98194996746229]
  Large language models (LLMs) are prone to hallucination and producing factually incorrect information. We propose a novel framework, called Think&Cite, and formulate attributed text generation as a multi-step reasoning problem integrated with search.
  arXiv  Detail & Related papers  (2024-12-19T13:55:48Z)
• Visual Agents as Fast and Slow Thinkers [88.1404921693082]
  We introduce FaST, which incorporates the Fast and Slow Thinking mechanism into visual agents. FaST employs a switch adapter to dynamically select between System 1/2 modes. It tackles uncertain and unseen objects by adjusting model confidence and integrating new contextual data.
  arXiv  Detail & Related papers  (2024-08-16T17:44:02Z)
• DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models [42.95876831743256]
  Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via Chains-of-Thought prompting. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex.
  arXiv  Detail & Related papers  (2024-07-01T06:45:13Z)
• What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
  We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models. We aim for the models to engage with and generate responses that span a wider contextual scene understanding. Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
  arXiv  Detail & Related papers  (2024-03-20T11:27:20Z)
• Fine-Grained Self-Endorsement Improves Factuality and Reasoning [72.83651220132495]
  This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. We propose a self-endorsement framework that leverages the fine-grained fact-level comparisons across multiple sampled responses.
  arXiv  Detail & Related papers  (2024-02-23T22:24:40Z)
• DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking [12.71072798544731]
  DUMA embodies a dual-mind mechanism through the utilization of two generative Large Language Models (LLMs) dedicated to fast and slow thinking respectively. We have constructed a conversational agent to handle online inquiries in the real estate industry.
  arXiv  Detail & Related papers  (2023-10-27T11:43:46Z)
• Towards Mitigating Hallucination in Large Language Models via Self-Reflection [63.2543947174318]
  Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. This paper analyses the phenomenon of hallucination in medical generative QA systems using widely adopted LLMs and datasets.
  arXiv  Detail & Related papers  (2023-10-10T03:05:44Z)
• Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning [16.495754104540605]
  Large language models (LLMs) can generate code-like plans for complex inference tasks such as visual reasoning. We propose a hierarchical plan-searching algorithm that integrates the one-stop reasoning (fast) and the Tree-of-thought (slow)
  arXiv  Detail & Related papers  (2023-08-18T16:21:40Z)

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.