Summary of One Thousand and One Pairs: a “novel” Challenge For Long-context Language Models, by Marzena Karpinska et al.

One Thousand and One Pairs: A “novel” challenge for long-context language models

by Marzena Karpinska, Katherine Thai, Kyle Lo, Tanya Goyal, Mohit Iyyer

First submitted to arxiv on: 24 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper presents a new dataset, NoCha, designed to test long-context language models’ (LLMs) ability to retrieve, synthesize, and reason over information across book-length inputs. Unlike existing synthetic benchmarks that only evaluate surface-level retrieval capabilities, NoCha contains 1,001 pairs of true and false claims about recently-published English fictional books, requiring global reasoning to verify. The authors evaluate ten LLMs, including GPT-4o, and find that none outperform random chance on the task, with GPT-4o achieving an accuracy of 55.8%. Analysis reveals models perform better on sentence-level retrieval and worse on speculative fiction books. The proposed methodology enables easy analysis of future models.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper creates a new way to test how well language models can understand big books. They make a dataset called NoCha with pairs of true and false claims about recently-published book series. Unlike previous tests that only looked at surface-level information, NoCha requires readers to think globally about the entire book. The authors tested 10 language models and found that none could do better than chance. They also discovered that models are better at understanding simple sentences rather than complex ideas from science fiction books.