CLIPPER: Compression enables long-context synthetic data generation

Chau Minh Pham, Yapei Chang, Mohit Iyyer

Keywords: synthetic data, fine-tuning, instruction-tuning

TL;DR: We introduce CLIPPER, a compression-based approach for generating synthetic data tailored to narrative claim verification—a task that requires reasoning over a book to verify a given claim.

Abstract: LLM developers are increasingly reliant on synthetic data, but generating high-quality data for complex long-context reasoning tasks remains challenging. We introduce CLIPPER, a compression-based approach for generating synthetic data tailored to narrative claim verification—a task that requires reasoning over a book to verify a given claim. Instead of generating claims directly from the raw text of the book, which results in artifact-riddled claims, CLIPPER first compresses the book into chapter outlines and book summaries and then uses these intermediate representations to generate complex claims and corresponding chain-of-thoughts. Compared to naive approaches, CLIPPER produces claims that are more valid, grounded, and complex. Using CLIPPER, we synthesize a dataset of 19K claims paired with source books and chain-of-thought reasoning, and use it to fine-tune three open-weight models. Our best model achieves breakthrough results on narrative claim verification (from 28% to 76% accuracy on our test set) and sets a new state-of-the-art for sub-10B models on the NoCha leaderboard. Further analysis shows that our models generate more detailed and grounded chain-of-thought reasoning while also improving performance on other narrative understanding tasks (e.g., NarrativeQA).

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Submission Number: 860