Does Finetuning with Scientific Data Increase Hallucinations? A Multi-domain Factuality Evaluation of LLMs

arXiv:2606.21359 · cs.CL · Submitted 2026-06-19 · Read on arXiv

cs.CL

Submitted: 2026-06-19

Updated: 2026-08-28

Code: https://github.com/ryabhmd/SciFactCheck

Terminology

• Hallucination — 56× in this paper · explained in 3 episodes
• Verification — 21× in this paper · explained in 2 episodes
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Abstention — 6× in this paper · explained in 1 episode
• Verifiability — 5× in this paper · explained in 2 episodes
• Perplexity — 4× in this paper · explained in 3 episodes
• RAG — 4× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Hallucination Detection — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Inter-Annotator Agreement — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Internet Encyclopedia of Philosophy — 1× in this paper
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• LLMs as Science Journalists: Supporting Early-stage Researchers in Communicating Their Science to the Public
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
• Qwen2.5-Coder Technical Report
• The 17% Gap: Quantifying Epistemic Decay in AI-Assisted Survey Papers
• Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
• OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation
• Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
• The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
• HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences
• MinerU: An Open-Source Solution for Precise Document Content Extraction
• Qwen3 Technical Report
• WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries

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