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Found 6 papers, 6 papers with code
CLIPPER: Compression enables long-context synthetic data generation
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.
PostMark: A Robust Blackbox Watermark for Large Language Models
The most effective techniques to detect LLM-generated text rely on inserting a detectable signature -- or watermark -- during the model's decoding process.
FABLES: Evaluating faithfulness and content selection in book-length summarization
While LLM-based auto-raters have proven reliable for factuality and coherence in other settings, we implement several LLM raters of faithfulness and find that none correlates strongly with human annotations, especially with regard to detecting unfaithful claims.
BooookScore: A systematic exploration of book-length summarization in the era of LLMs
We find that closed-source LLMs such as GPT-4 and Claude 2 produce summaries with higher BooookScore than those generated by open-source models.
RankGen: Improving Text Generation with Large Ranking Models
Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts.
RELIC: Retrieving Evidence for Literary Claims
Humanities scholars commonly provide evidence for claims that they make about a work of literature (e. g., a novel) in the form of quotations from the work.
