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

• URL: http://arxiv.org/abs/2401.04854v3
• Date: Mon, 3 Jun 2024 17:01:06 GMT
• Title: Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs
• Authors: Harvey Lederman, Kyle Mahowald,
• Abstract summary: We argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference. 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. 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.
• Score: 12.568491518122622
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

Related papers

• Large Language Models Reflect the Ideology of their Creators [73.25935570218375]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. We uncover notable diversity in the ideological stance exhibited across different LLMs and languages.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs [6.06227550292852]
  We study language ideologies in text produced by LLMs through a case study on English gendered language reform. We find political bias: when asked to use language that is "correct" or "natural", LLMs use language most similarly to when asked to align with conservative (vs. progressive) values. This shows how the language ideologies expressed in text produced by LLMs can vary, which may be unexpected to users.
  arXiv  Detail & Related papers  (2024-09-20T18:55:48Z)
• LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements [59.71218039095155]
  Task of reading comprehension (RC) provides a primary means to assess language models' natural language understanding (NLU) capabilities. If the context aligns with the models' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from internal information. To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities.
  arXiv  Detail & Related papers  (2024-04-09T13:08:56Z)
• Caveat Lector: Large Language Models in Legal Practice [0.0]
  The fascination with large language models derives from the fact that many users lack the expertise to evaluate the quality of the generated text. The dangerous combination of fluency and superficial plausibility leads to the temptation to trust the generated text and creates the risk of overreliance.
  arXiv  Detail & Related papers  (2024-03-14T08:19:41Z)
• LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks [18.068035947969044]
  There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification. We present a vision of bf LLM-Modulo Frameworks that combine the strengths of LLMs with external model-based verifiers.
  arXiv  Detail & Related papers  (2024-02-02T14:43:18Z)
• The ART of LLM Refinement: Ask, Refine, and Trust [85.75059530612882]
  We propose a reasoning with refinement objective called ART: Ask, Refine, and Trust. It asks necessary questions to decide when an LLM should refine its output. It achieves a performance gain of +5 points over self-refinement baselines.
  arXiv  Detail & Related papers  (2023-11-14T07:26:32Z)
• LLMs grasp morality in concept [0.46040036610482665]
  We provide a general theory of meaning that extends beyond humans. We suggest that the LLM, by virtue of its position as a meaning-agent, already grasps the constructions of human society. Unaligned models may help us better develop our moral and social philosophy.
  arXiv  Detail & Related papers  (2023-11-04T01:37:41Z)
• Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding [1.3654846342364308]
  Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. This position paper critically assesses three points recurring in critiques of LLM capacities. We outline a pragmatic perspective on the issue of real' understanding and intentionality in LLMs.
  arXiv  Detail & Related papers  (2023-10-30T15:51:04Z)
• Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation [80.126717170151]
  This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments. We introduce a novel framework, Recursive Contemplation (ReCon), to enhance LLMs' ability to identify and counteract deceptive information.
  arXiv  Detail & Related papers  (2023-10-02T16:27:36Z)
• LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? [52.71988102039535]
  We show that semantic censorship can be perceived as an undecidable problem. We argue that the challenges extend beyond semantic censorship, as knowledgeable attackers can reconstruct impermissible outputs.
  arXiv  Detail & Related papers  (2023-07-20T09:25:02Z)
• In-Context Impersonation Reveals Large Language Models' Strengths and Biases [56.61129643802483]
  We ask LLMs to assume different personas before solving vision and language tasks. We find that LLMs pretending to be children of different ages recover human-like developmental stages. In a language-based reasoning task, we find that LLMs impersonating domain experts perform better than LLMs impersonating non-domain experts.
  arXiv  Detail & Related papers  (2023-05-24T09:13:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.