For Those Who May Find Themselves on the Red Team
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Abstract (arXiv)
This position paper argues that literary scholars must engage with large language model (LLM) interpretability research. While doing so will involve ideological struggle, if not out-right complicity, the necessity of this engagement is clear: the abiding instrumentality of current approaches to interpretability cannot be the only standard by which we measure interpretation with LLMs. One site at which this struggle could take place, I suggest, is the red team.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 23 Nov 2025.
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6 of 18 references.
References · 6
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
- 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 434
- 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 327
- 2024ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMscited by 216
- 2023Effective Prompt Extraction from Language Modelscited by 85
- 2020Universal Adversarial Attacks with Natural Triggers for Text Classificationcited by 51
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- 2026From annotation to reasoning: Culture in language models
- 2025KRISTEVA: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning
- 2024Rethinking Interpretability in the Era of Large Language Models
- 2025Because we have LLMs, we Can and Should Pursue Agentic Interpretability
- 2024Awes, Laws, and Flaws From Today's LLM Research
- 2025Literary Evidence Retrieval via Long-Context Language Models
- 2025Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives
- 2024Automating Thematic Analysis: How LLMs Analyse Controversial Topics