LLM Abstention

LLM: Large Language Model

Large language model abstention studies how systems can recognize missing knowledge, insufficient evidence, or unreliable reasoning and choose whether to answer, clarify, defer, or stop. Methods include confidence and consistency signals, evidence checks and internal-state probes, post-hoc selection thresholds, and training with supervised or reinforcement-learning rewards for calibrated abstention. Benchmarks test factual question answering, retrieval-augmented generation, clinical decisions, and tool-using agents, measuring both answer quality and appropriate abstention. A central tension is balancing coverage against the risk of unsupported answers: models may recognize uncertainty but still commit, or abstain so often that useful answers are lost, especially when settings change.

Latest papers

99 papers20 papers in the last four weeks, up 54% on the four weeks before. 0.1% of all new papers.

Hallucination as output-boundary misclassification: a composite abstention architecture for language models
Mar 12, 2026Angelina HintsanenLLM Hallucination Mitigation Hallucination in Language Models
NEXUS Laboratory, Helsinki, Finland
LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation
Mar 6, 2026Koki Itai, Shunichi Hasegawa, Yuta Yamamoto +2RAG Evaluation LLM Evaluation
neoAI Inc., Tokyo, Japan · Tokyo Metropolitan University, Tokyo, Japan · The University of Tokyo, Tokyo, Japan
ClinDet-Bench: Beyond Abstention, Evaluating Judgment Determinability of LLMs in Clinical Decision-Making
Feb 26, 2026Yusuke Watanabe, Yohei Kobashi, Takeshi Kojima +3Clinical Language Model Evaluation LLM Safety Benchmarks
Kyoto University, Department of Biomedical Data Intelligence · Kyoto University, Department of Cardiovascular Medicine · The University of Tokyo
ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
Feb 24, 2026Sunguk Shin, Meeyoung Cha, Byung-Jun Lee +1RAG Robustness LLM Abstention
Korea University, Seoul, South Korea · MPI-SP, Bochum, Germany · KAIST, Daejeon, South Korea +1
Improving LLM Reliability through Hybrid Abstention and Adaptive Detection
Feb 17, 2026Ankit Sharma, Nachiket Tapas, Jyotiprakash PatraLLM Guardrails LLM Reliability
Department of Computer Science & Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"
Feb 4, 2026Dhruv Madhwal, Lyuxin David Zhang, Dan Roth +2LLM Prompting Question Decomposition
Arizona State University · University of Pennsylvania · Oracle AI
Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation
Nov 14, 2025Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan LiLLM Abstention LLM Hallucination Mitigation
Toyota Technological Institute at Chicago · University of California, San Diego
Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety
Oct 18, 2025Vamshi Krishna Bonagiri, Ponnurangam Kumaragurum, Khanh Nguyen +1LLM Agent Evaluation Tool-Using Agents
University of California, Berkeley · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · International Institute of Information Technology, Hyderabad (IIIT Hyderabad)
When Guessing is Rewarded: Rethinking Language Model Evaluation with Distributional Uncertainty Scoring
Oct 5, 2025Thomas F BurnsLLM Evaluation Language Model Generation Evaluation
Aleph Alpha Research
LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty
Jul 22, 2025Zipeng Ling, Shuliang Liu, Yuehao Tang +8LLM Abstention LLM Uncertainty Estimation
The Hong Kong University of Science and Technology (Guangzhou) · University of Alberta · Alberta Machine Intelligence Institute +5
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
May 3, 2024Deepa Tilwani, Yash Saxena, Seyedali Mohammadi +5Citation Verification RAG Evaluation
Artificial Intelligence Institute, University of South Carolina, Columbia, South Carolina, USA · University of Maryland, Baltimore County, Baltimore, Maryland, USA · IAIRO, India +1

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Hallucination as output-boundary misclassification: a composite abstention architecture for language models

Angelina Hintsanen

NEXUS Laboratory, Helsinki, Finland

LLM Hallucination Mitigation Hallucination in Language Models LLM Abstention Refusal Mechanisms in Language Models Refusal Behavior in Language Models Question Answering

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Large language models often produce unsupported claims. We frame this as a misclassification error at the output boundary, where internally generated completions are emitted as if they were grounded in evidence. This motivates a composite intervention that combines instruction-based refusal with a structural abstention gate. The gate computes a support deficit score, St, from three black-box signals: self-consistency (At), paraphrase stability (Pt), and citation coverage (Ct), and blocks output when St exceeds a threshold. In a controlled evaluation across 50 items, five epistemic regimes, and three models, neither mechanism alone was sufficient. Instruction-only prompting reduced hallucination sharply, but still showed over-cautious abstention on answerable items and residual hallucination for GPT-3.5-turbo. The structural gate preserved answerable accuracy across models but missed confident confabulation on conflicting-evidence items. The composite architecture achieved high overall accuracy with low hallucination, while also inheriting some over-abstention from the instruction component. A supplementary 100-item no-context stress test derived from TruthfulQA showed that structural gating provides a capability-independent abstention floor. Overall, instruction-based refusal and structural gating show complementary failure modes, which suggests that effective hallucination control benefits from combining both mechanisms.