Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism

• URL: http://arxiv.org/abs/2311.01041v3
• Date: Wed, 29 May 2024 09:19:35 GMT
• Title: Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism
• Authors: Lang Cao,
• Abstract summary: Large language models (LLMs) have demonstrated impressive language understanding and generation capabilities. These models are not flawless and often produce responses that contain errors or misinformation. We propose a refusal mechanism that instructs LLMs to refuse to answer challenging questions in order to avoid errors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, enabling them to answer a wide range of questions across various domains. However, these models are not flawless and often produce responses that contain errors or misinformation. These inaccuracies, commonly referred to as hallucinations, render LLMs unreliable and even unusable in many scenarios. In this paper, our focus is on mitigating the issue of hallucination in LLMs, particularly in the context of question-answering. Instead of attempting to answer all questions, we explore a refusal mechanism that instructs LLMs to refuse to answer challenging questions in order to avoid errors. We then propose a simple yet effective solution called Learn to Refuse (L2R), which incorporates the refusal mechanism to enable LLMs to recognize and refuse to answer questions that they find difficult to address. To achieve this, we utilize a structured knowledge base to represent all the LLM's understanding of the world, enabling it to provide traceable gold knowledge. This knowledge base is separate from the LLM and initially empty. It can be filled with validated knowledge and progressively expanded. When an LLM encounters questions outside its domain, the system recognizes its knowledge scope and determines whether it can answer the question independently. Additionally, we introduce a method for automatically and efficiently expanding the knowledge base of LLMs. Through qualitative and quantitative analysis, we demonstrate that our approach enhances the controllability and reliability of LLMs.

Related papers

• Are LLMs Aware that Some Questions are not Open-ended? [58.93124686141781]
  We study whether Large Language Models are aware that some questions have limited answers and need to respond more deterministically. The lack of question awareness in LLMs leads to two phenomena: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions.
  arXiv  Detail & Related papers  (2024-10-01T06:07:00Z)
• Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals [53.273592543786705]
  Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application. We propose CoKE, which first probes LLMs' knowledge boundary via internal confidence given a set of questions, and then leverages the probing results to elicit the expression of the knowledge boundary.
  arXiv  Detail & Related papers  (2024-06-16T10:07:20Z)
• LLM-Generated Black-box Explanations Can Be Adversarially Helpful [16.49758711633611]
  Large Language Models (LLMs) help us solve and understand complex problems by acting as digital assistants. Our research uncovers a hidden risk tied to this approach, which we call *adversarial helpfulness*. This happens when an LLM's explanations make a wrong answer look right, potentially leading people to trust incorrect solutions.
  arXiv  Detail & Related papers  (2024-05-10T20:23:46Z)
• Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
  Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge. We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
  arXiv  Detail & Related papers  (2024-04-04T16:40:11Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method [36.24876571343749]
  Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. Recent literature reveals that LLMs generate nonfactual responses intermittently. We propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
  arXiv  Detail & Related papers  (2023-10-27T06:22:14Z)
• Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs [19.0797968186656]
  Large language models (LLMs) are versatile and can solve different tasks due to their emergent ability and generalizability. In some previous works, additional modules like graph neural networks (GNNs) are trained on retrieved knowledge from external knowledge bases.
  arXiv  Detail & Related papers  (2023-09-06T15:55:01Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.