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Computer Science > Computation and Language

arXiv:2404.01261 (cs)
[Submitted on 1 Apr 2024]

Title:FABLES: Evaluating faithfulness and content selection in
book-length summarization

Authors:Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella
, Varun Manjunatha, Kyle Lo, Tanya Goyal, Mohit Iyyer
View a PDF of the paper titled FABLES: Evaluating faithfulness and
content selection in book-length summarization, by Yekyung Kim and 7
other authors
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    Abstract:While long-context large language models (LLMs) can
    technically summarize book-length documents (>100K tokens), the
    length and complexity of the documents have so far prohibited
    evaluations of input-dependent aspects like faithfulness. In this
    paper, we conduct the first large-scale human evaluation of
    faithfulness and content selection on LLM-generated summaries of
    fictional books. Our study mitigates the issue of data
    contamination by focusing on summaries of books published in 2023
    or 2024, and we hire annotators who have fully read each book
    prior to the annotation task to minimize cost and cognitive
    burden. We collect FABLES, a dataset of annotations on 3,158
    claims made in LLM-generated summaries of 26 books, at a cost of
    $5.2K USD, which allows us to rank LLM summarizers based on
    faithfulness: Claude-3-Opus significantly outperforms all
    closed-source LLMs, while the open-source Mixtral is on par with
    GPT-3.5-Turbo. An analysis of the annotations reveals that most
    unfaithful claims relate to events and character states, and they
    generally require indirect reasoning over the narrative to
    invalidate. 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. Our experiments suggest that detecting
    unfaithful claims is an important future direction not only for
    summarization evaluation but also as a testbed for long-context
    understanding. Finally, we move beyond faithfulness by exploring
    content selection errors in book-length summarization: we develop
    a typology of omission errors related to crucial narrative
    elements and also identify a systematic over-emphasis on events
    occurring towards the end of the book.

Comments: preprint - 39 pages
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2404.01261 [cs.CL]
          (or arXiv:2404.01261v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2404.01261
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yekyung Kim [view email]
[v1] Mon, 1 Apr 2024 17:33:38 UTC (20,930 KB)
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    Kim and 7 other authors
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