SPIN Unprocessed October 21, 2026 ai_technology research
Fluent but Fallible: Benchmarking Hallucination and Factual Reliability in Large Language Models
View original on arxiv.orgOverview
arXiv:2610.27034v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly advanced natural language processing and artificial intelligence applications through their capabilities in reasoning, text generation and knowledge retrieval. However, LLMs are still vulnerable to two important challenges: hallucination, generating factually incorrect or fabricated content, and response deviation, meaning systematic deviations from ground-truth responses. These limitations raise seri
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Computation and Language
View all →- A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting
- Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA Models
- Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding
- Language Impairment as a Lens onto Large Language Models
- Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG
- Guilt by Association: Semantic Leakage Drives Associative Gender Bias
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO