Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs
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Abstract (arXiv)
Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be explained if LLMs were not cultural technologies but had beliefs, desires, and intentions. According to interpretationism in the philosophy of mind, a system has such attitudes if and only if its behavior is well explained by the hypothesis that it does. Interpretationists may hold that LLMs have attitudes, and thus have a simple solution to the novel reference problem. We emphasize, however, that interpretationism is compatible with very simple creatures having attitudes and differs sharply from views that presuppose these attitudes require consciousness, sentience, or intelligence (topics about which we make no claims).
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 26 of its 72 references that are in the map. First posted on arXiv 10 Jan 2024.
Builds on
26 of 72 references.
References · 26
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
- 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
- 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
- 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
- 2023Role-Play with Large Language Modelscited by 290
- 2020Experience Grounds Languagecited by 241
- 2018Colorless green recurrent networks dream hierarchicallycited by 210
- 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
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- 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 99
- 2019The emergence of number and syntax units in LSTM language modelscited by 92
- 2023Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblockscited by 77
- 2022Meaning without reference in large language modelscited by 76
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- 2020Word meaning in minds and machinescited by 51
- 2023From task structures to world models: What do LLMs know?cited by 46
- 2023Entity Tracking in Language Modelscited by 44
- 2023The Vector Grounding Problemcited by 39
- 2023Consistency Analysis of ChatGPTcited by 37
- 2024A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debatescited by 22
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Citing papers per year: 2024: 4; 2025: 2; 2026: 5.
11 papers.
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- 2026On the Credibility of Evaluating LLMs using Survey Questionscited by 3
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- 2026Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigationscited by 0
- 2026Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations29 Sep 2026
- 2026Map Users and Mapmakers: The Scope of Cognitive Attribution from Acquired Representations12 Sep 2026
- 2026Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models14 Aug 2026
- 2026Chatbots Output Meaningful (but Problematic) Language2 Jun 2026
- 2026On the Credibility of Evaluating LLMs using Survey Questions3 Feb 2026
- 2025Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations29 May 2025
- 2025RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles23 Jan 2025
- 2024Do Large Language Models Advocate for Inferentialism?19 Dec 2024
- 2024Does ChatGPT Have a Mind?27 Jun 2024
- 2024Large language models and linguistic intentionality15 Apr 2024
- 2024Monotonic Representation of Numeric Properties in Language Models15 Mar 2024