LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization

arXiv:2608.18082 · cs.CL · Submitted 2026-06-04 · Read on arXiv

cs.CL

Submitted: 2026-06-04

Updated: 2026-09-19

Comments: Accepted at EMNLP 2026

Code: https://github.com/BDML-lab/LongNovel

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Hallucination — 370× in this paper · explained in 3 episodes
• Hallucination Detection — 36× in this paper · explained in 2 episodes
• Benchmark — 35× in this paper · explained in 2 episodes
• Consistency — 26× in this paper · explained in 3 episodes
• RAG — 24× in this paper · explained in 3 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Generalization — 7× in this paper · explained in 13 episodes
• Multi-Model Arbitration — 7× in this paper
• Core — 5× in this paper · explained in 2 episodes
• Entity-Referenced Hallucination Generation — 4× in this paper
• Verification — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 3× in this paper · explained in 4 episodes
• Chain-of-Thought Prompting — 3× in this paper · explained in 2 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) Prompting — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Retrieval-Augmented Generation — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Supervised Fine-Tuning — 1× in this paper
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
• The Llama 3 Herd of Models
• FABLES: Evaluating faithfulness and content selection in book-length summarization
• Ring Attention with Blockwise Transformers for Near-Infinite Context
• CLIPPER: Compression enables long-context synthetic data generation
• Fine-grained Hallucination Detection and Editing for Language Models
• Uncertainty Resolution in Misinformation Detection
• Giraffe: Adventures in Expanding Context Lengths in LLMs

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