# Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs

arXiv: https://arxiv.org/abs/2401.04854 · HTML: https://mlsanity.com/paper/2401.04854/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 72 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Solving Quantitative Reasoning Problems with Language Models](https://mlsanity.com/paper/2206.14858/) (2022)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Probing Classifiers: Promises, Shortcomings, and Advances](https://mlsanity.com/paper/2102.12452/) (2021)
- [Measuring and Improving Consistency in Pretrained Language Models](https://mlsanity.com/paper/2102.01017/) (2021)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN](https://mlsanity.com/paper/2111.09509/) (2021)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://mlsanity.com/paper/2305.08809/) (2023)
- [The emergence of number and syntax units in LSTM language models](https://mlsanity.com/paper/1903.07435/) (2019)
- [Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblocks](https://mlsanity.com/paper/2307.00175/) (2023)
- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Do Large Language Models know what humans know?](https://mlsanity.com/paper/2209.01515/) (2022)
- [Word meaning in minds and machines](https://mlsanity.com/paper/2008.01766/) (2020)
- [From task structures to world models: What do LLMs know?](https://mlsanity.com/paper/2310.04276/) (2023)
- [Entity Tracking in Language Models](https://mlsanity.com/paper/2305.02363/) (2023)
- [The Vector Grounding Problem](https://mlsanity.com/paper/2304.01481/) (2023)
- [Consistency Analysis of ChatGPT](https://mlsanity.com/paper/2303.06273/) (2023)
- [A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates](https://mlsanity.com/paper/2401.03910/) (2024)

## Built on by (most cited first, 11 of 11)

- [Monotonic Representation of Numeric Properties in Language Models](https://mlsanity.com/paper/2403.10381/) (2024)
- [Does ChatGPT Have a Mind?](https://mlsanity.com/paper/2407.11015/) (2024)
- [Large language models and linguistic intentionality](https://mlsanity.com/paper/2404.09576/) (2024)
- [Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations](https://mlsanity.com/paper/2506.00074/) (2025)
- [On the Credibility of Evaluating LLMs using Survey Questions](https://mlsanity.com/paper/2602.04033/) (2026)
- [Do Large Language Models Advocate for Inferentialism?](https://mlsanity.com/paper/2412.14501/) (2024)
- [RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles](https://mlsanity.com/paper/2501.13491/) (2025)
- [Chatbots Output Meaningful (but Problematic) Language](https://mlsanity.com/paper/2606.02973/) (2026)
- [Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models](https://mlsanity.com/paper/2608.14252/) (2026)
- [Map Users and Mapmakers: The Scope of Cognitive Attribution from Acquired Representations](https://mlsanity.com/paper/2609.13879/) (2026)
- [Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations](https://mlsanity.com/paper/2609.38486/) (2026)

## 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).