Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge.
Long novels are better suited than news or papers for researching these hallucinations, due to their intrinsic information and detailed descriptions of events and dialogues.
However, current research lacks a multi-scale benchmark for hallucination detection in long-context novel summarization and does not fully explore how hallucinations change as the context grows longer.
In this Study
In this study, we propose LongNovel, a multi-scale long-context bilingual (Chinese and English) novel benchmark for hallucination detection.
This benchmark is constructed from 29 Chinese novels (ranging from 16k to 100k tokens) and chapter-level data from the BookSum dataset.
We design 8 hallucination types and employ a combination of Multi-Model Arbitration and Entity-Referenced Hallucination Generation to ensure both data authenticity and a balanced distribution of hallucination categories.
Furthermore, we manually revise the content in the test set to guarantee data reliability.
Experimental Results
Extensive experimental results demonstrate that LongNovel is a challenging benchmark.
We release LongNovel for future research.