Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2604.05483v1
• Date: Tue, 07 Apr 2026 06:24:01 GMT
• Title: Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning
• Abstract summary: Large Language Models (LLMs) have shown a high capability in answering questions on a diverse range of topics.<n>LLMs sometimes produce biased, ideologized or incorrect responses, limiting their applications if there is no clear understanding of which topics their answers can be trusted.<n>We introduce a novel algorithm, named as GMRL-BD, designed to identify the untrustworthy boundaries (in terms of topics) of a given LLM.
• Score: 22.559085865596728
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown a high capability in answering questions on a diverse range of topics. However, these models sometimes produce biased, ideologized or incorrect responses, limiting their applications if there is no clear understanding of which topics their answers can be trusted. In this research, we introduce a novel algorithm, named as GMRL-BD, designed to identify the untrustworthy boundaries (in terms of topics) of a given LLM, with black-box access to the LLM and under specific query constraints. Based on a general Knowledge Graph (KG) derived from Wikipedia, our algorithm incorporates with multiple reinforcement learning agents to efficiently identify topics (some nodes in KG) where the LLM is likely to generate biased answers. Our experiments demonstrated the efficiency of our algorithm, which can detect the untrustworthy boundary with just limited queries to the LLM. Additionally, we have released a new dataset containing popular LLMs including Llama2, Vicuna, Falcon, Qwen2, Gemma2 and Yi-1.5, along with labels indicating the topics on which each LLM is likely to be biased.

Related papers

• Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena [23.264049073539663]
  Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs) LLMs may inherently favor certain answer choice IDs, such as A/B/C/D, due to inherent biases of priori unbalanced probabilities. This work aims to tackle these significant difficulties, and establish a new LLM evaluation benchmark through entirely open-style questions.
  arXiv  Detail & Related papers  (2024-06-11T17:59:47Z)
• Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering [67.94354589215637]
  Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations. In this paper, we perceive the LLMs' knowledge boundary (KB) with semi-open-ended questions (SoeQ) We find that GPT-4 performs poorly on SoeQ and is often unaware of its KB. Our auxiliary model, LLaMA-2-13B, is effective in discovering more ambiguous answers.
  arXiv  Detail & Related papers  (2024-05-23T10:00:14Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering [18.94220625114711]
  Large language models (LLMs) perform surprisingly well and outperform human experts on many tasks. This paper integrates and optimized a pipeline for selecting reasoning paths from KG based on LLM. We also propose a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank.
  arXiv  Detail & Related papers  (2024-04-16T08:28:16Z)
• 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)
• Investigating Answerability of LLMs for Long-Form Question Answering [35.41413072729483]
  We focus on long-form question answering (LFQA) because it has several practical and impactful applications. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting.
  arXiv  Detail & Related papers  (2023-09-15T07:22:56Z)
• 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)
• Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models [37.63939774027709]
  Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities. We propose and compare several confidence/uncertainty measures, applying them to *selective NLG* where unreliable results could either be ignored or yielded for further assessment. Results reveal that a simple measure for the semantic dispersion can be a reliable predictor of the quality of LLM responses.
  arXiv  Detail & Related papers  (2023-05-30T16:31:26Z)
• Statistical Knowledge Assessment for Large Language Models [79.07989821512128]
  Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? We propose KaRR, a statistical approach to assess factual knowledge for LLMs. Our results reveal that the knowledge in LLMs with the same backbone architecture adheres to the scaling law, while tuning on instruction-following data sometimes compromises the model's capability to generate factually correct text reliably.
  arXiv  Detail & Related papers  (2023-05-17T18:54:37Z)

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.