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

Harvey Lederman, Kyle Mahowald

TL;DR

The paper investigates whether large language models (LLMs) function as cultural technologies under bibliotechnism, producing meaning derivatively from human-generated training data. It argues that derivatively meaningful text can arise via causal histories and intelligibility, enabling even entirely novel outputs to be meaningful without attributing beliefs, desires, or intentions to LLMs. A second, distinct challenge—the novel reference problem—shows that LLMs can generate new names or referents not present in training data, which would be hard to explain under a purely derivative account unless attitudes are invoked. The authors defend interpretationism as a lightweight framework in which LLM behavior can be well explained by possession of beliefs, desires, and intentions, without requiring consciousness, and they discuss how reader-intentions and metametac semantics may ground novel reference. Overall, the work argues for high-level, attitude-based explanations as productive tools for understanding LLMs while clarifying their limitations and the role of cultural-technology perspectives in AI semantics.

Abstract

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).

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

TL;DR

The paper investigates whether large language models (LLMs) function as cultural technologies under bibliotechnism, producing meaning derivatively from human-generated training data. It argues that derivatively meaningful text can arise via causal histories and intelligibility, enabling even entirely novel outputs to be meaningful without attributing beliefs, desires, or intentions to LLMs. A second, distinct challenge—the novel reference problem—shows that LLMs can generate new names or referents not present in training data, which would be hard to explain under a purely derivative account unless attitudes are invoked. The authors defend interpretationism as a lightweight framework in which LLM behavior can be well explained by possession of beliefs, desires, and intentions, without requiring consciousness, and they discuss how reader-intentions and metametac semantics may ground novel reference. Overall, the work argues for high-level, attitude-based explanations as productive tools for understanding LLMs while clarifying their limitations and the role of cultural-technology perspectives in AI semantics.

Abstract

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).
Paper Structure (21 sections)