@inproceedings{sui-etal-2025-kristeva,
    title = "{KRISTEVA}: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning",
    author = "Sui, Peiqi  and
      Rodriguez, Juan Diego  and
      Laban, Philippe  and
      Murphy, J. Dean  and
      Dexter, Joseph P.  and
      So, Richard Jean  and
      Baker, Samuel  and
      Chaudhuri, Pramit",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1577/",
    pages = "32829--32849",
    ISBN = "979-8-89176-251-0",
    abstract = "Each year, tens of millions of essays are written and graded in college-level English courses. Students are asked to analyze literary and cultural texts through a process known as close reading, where they gather textual details from which to formulate evidence-based arguments. Despite being viewed as a basis for critical thinking and widely adopted as a required element of university coursework, close reading has never been evaluated on large language models (LLMs), and multi-discipline benchmarks like MMLU do not include literature as a subject. To fill this gap, we present KRISTEVA, the first close reading benchmark for evaluating interpretive reasoning, consisting of 1331 multiple-choice questions adapted from classroom data. With KRISTEVA, we propose three progressively more difficult sets of tasks to approximate different elements of the close reading process, which we use to test how well LLMs understand and reason about literary works: 1) extracting stylistic features, 2) retrieving relevant contextual information from parametric knowledge, and 3) multi-hop reasoning between style and external contexts. Our baseline results find that while state-of-the-art LLMs possess some college-level close reading competency (accuracy 49.7{\%} - 69.7{\%}), their performances still trail those of experienced human evaluators on 10 out of our 11 tasks."
}