mlsanityData to 7 Oct 2026

Recognizing Limits: Investigating Infeasibility in Large Language Models

arXiv 2408.05873 ↗First posted 11 Aug 2024cs.CLcited by 4

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Abstract (arXiv)

Large language models (LLMs) have shown remarkable performance in various tasks but often fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. This paper addresses the need for LLMs to recognize and refuse infeasible tasks due to the requests surpassing their capabilities. We conceptualize four main categories of infeasible tasks for LLMs, which cover a broad spectrum of hallucination-related challenges identified in prior literature. We develop and benchmark a new dataset comprising diverse infeasible and feasible tasks to evaluate multiple LLMs' abilities to decline infeasible tasks. Furthermore, we explore the potential of increasing LLMs' refusal capabilities with fine-tuning. Our experiments validate the effectiveness of the trained models, suggesting promising directions for improving the performance of LLMs in real-world applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 64 references that are in the map. First posted on arXiv 11 Aug 2024.

Builds on

33 of 64 references.

References · 33

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
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  12. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
21 more
  1. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  2. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  3. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 755
  4. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  5. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 659
  6. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  7. 2021Revisiting the Calibration of Modern Neural Networkscited by 387
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  10. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
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  12. 2024Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaborationcited by 176
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  14. 2022Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysiscited by 104
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  16. 2023Enhancing Uncertainty-Based Hallucination Detection with Stronger Focuscited by 98
  17. 2024Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Taskscited by 51
  18. 2023Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanismcited by 33
  19. 2023Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledgecited by 32
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024Dual Active Learning for Reinforcement Learning from Human Feedbackcited by 19
  2. 2025PersonaEval: Are LLM Evaluators Human Enough to Judge Role-Play?cited by 9
  3. 2026From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skillscited by 5
  4. 2026Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoningcited by 0