Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer

arXiv cs.CL·Jewon Yeom, Jaewon Sok, Heejun Kim, Seonghyeon Park, Jeongjae Park, Taesup Kim

15h ago

·~1 min·5/22/2026·en·0

Quick Take

Larger LLMs exhibit hallucinations despite knowing answers due to probability distribution issues.

Key Points

Hallucinations occur even with correct concepts available.
Probability mass distribution distinguishes correct answers from hallucinations.
Instruction tuning enhances answer commitment with model scale.

Reader Mode unavailable (could not extract clean content).

Read on arxiv.org

Want this in your inbox every morning?

Daily brief at your local 8am — bilingual EN/中文, free.

Subscribe — it's free

More from arXiv cs.CL

See more →

arXiv cs.CL·Leyao Wang, Yanan He, Peng Chen, Asaf Yehudai, Yixin Liu, Rex Ying, Michal Shmueli-Scheuer, Arman Cohan

2d ago

FeaturedOriginal

Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?

AI Summary

The reliability of LLM judges for evaluating deep research agents is critically assessed using the REFLECT benchmark.

#LLM #Agent #Inference #Policy

2

arXiv cs.CL·Xiaoou Liu, Tiejin Chen, Dengjia Zhang, Yaqing Wang, Lu Cheng, Hua Wei

2d ago

FeaturedOriginal

Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

AI Summary

The Stepwise Confidence Attribution framework enhances diagnosis of reasoning failures in black-box LLMs.

#LLM #Inference #Open Source

4

arXiv cs.CL·Geoffrey Martin, Xuan Zhong Feng, Yifan Peng

15h ago

FeaturedOriginal

Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity

AI Summary

LLMs outperform fine-tuned models in extracting complex circumstances from NVDRS data.

#LLM #AI Coding #Inference

0

Related in this space

See more →

arXiv cs.AI·Angelos Angelopoulos, James F. Cahoon, Ron Alterovitz

3d ago

FeaturedOriginal

From Prompts to Protocols: An AI Agent for Laboratory Automation

AI Summary

An AI agent integrates large language models for automating laboratory protocols, enhancing efficiency and accuracy.

#LLM #Agent #AI Coding #Enterprise AI

1

arXiv cs.AI·Yihan Xia, Panpan You, Taotao Wang, Fang Liu, Han Qi, Xiaoxiao Wu, Shengli Zhang

2d ago

FeaturedOriginal

Agentic Trading: When LLM Agents Meet Financial Markets

AI Summary

The paper reviews LLM-based trading agents, highlighting protocol incomparability and reproducibility challenges.

#LLM #Agent #AI Startup #Enterprise AI

3

67

Business impact20%0

Novelty (recency)10%96

≥75 high · 50–74 medium · <50 low

Why Featured

This highlights the need for developers and PMs to address hallucination risks in LLMs, impacting product reliability and user trust, while investors should consider the implications for AI model investment strategies.