HalluPeer: A Taxonomy-driven Benchmark for Detecting Hallucinations in Scientific Peer Reviews

arXiv:2609.03580 · cs.AI, cs.CL · Submitted 2026-09-03 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-03

Updated: 2026-10-07

Code: https://github.com/Lin-TzuLing/HalluPeer

Terminology

Hallucination — 300× in this paper · explained in 3 episodes
Verification — 41× in this paper · explained in 2 episodes
Hallucination Detection — 25× in this paper · explained in 2 episodes
Benchmark — 24× in this paper · explained in 2 episodes
Grounding — 16× in this paper · explained in 3 episodes
Verifier — 16× in this paper · explained in 2 episodes
Fine-Tuning — 13× in this paper · explained in 5 episodes
Accuracy — 12× in this paper · explained in 1 episode
Core — 12× in this paper · explained in 2 episodes
Ablation — 11× in this paper · explained in 1 episode
Alignment — 10× in this paper · explained in 1 episode
Consistency — 9× in this paper · explained in 3 episodes
Large Language Model — 8× in this paper
F1 Score — 7× in this paper · explained in 3 episodes
Quantization — 7× in this paper · explained in 12 episodes
Abstraction — 5× in this paper · explained in 1 episode
Paper Review — 5× in this paper · explained in 2 episodes
Adaptation — 4× in this paper · explained in 1 episode
Diffusion Model — 4× in this paper · explained in 8 episodes
RAG — 4× in this paper · explained in 3 episodes
Redundancy — 4× in this paper · explained in 2 episodes
Diffusion Models — 3× in this paper · explained in 6 episodes
Generalization — 3× in this paper · explained in 13 episodes
Robustness — 3× in this paper · explained in 5 episodes
Transferability — 3× in this paper · explained in 4 episodes
Ablation study — 2× in this paper · explained in 1 episode
Benchmarking — 2× in this paper · explained in 3 episodes
Contrastive Learning — 2× in this paper · explained in 5 episodes
Cosine Similarity — 2× in this paper · explained in 3 episodes
Failure Modes — 2× in this paper · explained in 3 episodes
Federated Learning — 2× in this paper · explained in 14 episodes
Generative Model — 2× in this paper · explained in 2 episodes
Generative Models — 2× in this paper · explained in 2 episodes
Knowledge Retrieval — 2× in this paper
LLM judge — 2× in this paper · explained in 2 episodes
LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
Matthews Correlation Coefficient — 2× in this paper
Self-Supervised Learning — 2× in this paper · explained in 7 episodes
Transformer — 2× in this paper · explained in 2 episodes
Actionability — 1× in this paper · explained in 3 episodes
AdamW Optimizer — 1× in this paper · explained in 1 episode
Alpha Parameter — 1× in this paper · explained in 2 episodes
Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
Commentary — 1× in this paper · explained in 2 episodes
Error Taxonomy — 1× in this paper · explained in 2 episodes
Federated Learning (FL) — 1× in this paper · explained in 7 episodes
Human-in-the-Loop — 1× in this paper · explained in 3 episodes
Instruction Tuning — 1× in this paper · explained in 2 episodes
Interpretability — 1× in this paper · explained in 15 episodes
LLM Integration — 1× in this paper · explained in 2 episodes
Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
LoRA Adapters — 1× in this paper · explained in 3 episodes
Longest Common Subsequence — 1× in this paper
Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
Regularization — 1× in this paper · explained in 3 episodes
Selection Bias — 1× in this paper · explained in 4 episodes
Semantic Understanding — 1× in this paper · explained in 2 episodes
Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
Tokenization — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
alpha — 1× in this paper · explained in 1 episode

Terminology

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