A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates

• URL: http://arxiv.org/abs/2401.03910v1
• Date: Mon, 8 Jan 2024 14:12:31 GMT
• Title: A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates
• Authors: Rapha\"el Milli\`ere, Cameron Buckner
• Abstract summary: This article serves both as a primer on language models for philosophers, and as an opinionated survey of their significance. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models.
• Score: 0.05657375260432172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the extent to which we can meaningfully ascribe any kind of linguistic or cognitive competence to language models. Such questions have deep philosophical roots, echoing longstanding debates about the status of artificial neural networks as cognitive models. This article -- the first part of two companion papers -- serves both as a primer on language models for philosophers, and as an opinionated survey of their significance in relation to classic debates in the philosophy cognitive science, artificial intelligence, and linguistics. We cover topics such as compositionality, language acquisition, semantic competence, grounding, world models, and the transmission of cultural knowledge. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. However, we also highlight the need for further empirical investigation to better understand their internal mechanisms. This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models, and new philosophical questions prompted by their latest developments.

Related papers

• Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning [84.94709351266557]
  We focus on the trustworthiness of language models with respect to retrieval augmentation. We deem that retrieval-augmented language models have the inherent capabilities of supplying response according to both contextual and parametric knowledge. Inspired by aligning language models with human preference, we take the first step towards aligning retrieval-augmented language models to a status where it responds relying merely on the external evidence.
  arXiv  Detail & Related papers  (2024-10-22T09:25:21Z)
• Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024) [16.282850445579857]
  Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically. Recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities. Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning.
  arXiv  Detail & Related papers  (2024-10-07T02:31:47Z)
• Language Evolution with Deep Learning [49.879239655532324]
  Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
  arXiv  Detail & Related papers  (2024-03-18T16:52:54Z)
• Visually Grounded Language Learning: a review of language games, datasets, tasks, and models [60.2604624857992]
  Many Vision+Language (V+L) tasks have been defined with the aim of creating models that can ground symbols in the visual modality. In this work, we provide a systematic literature review of several tasks and models proposed in the V+L field.
  arXiv  Detail & Related papers  (2023-12-05T02:17:29Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• Dissociating language and thought in large language models [52.39241645471213]
  Large Language Models (LLMs) have come closest among all models to date to mastering human language. We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty.
  arXiv  Detail & Related papers  (2023-01-16T22:41:19Z)
• Language Models as Inductive Reasoners [125.99461874008703]
  We propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts. We create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language. We provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts.
  arXiv  Detail & Related papers  (2022-12-21T11:12:14Z)
• Integrating Linguistic Theory and Neural Language Models [2.870517198186329]
  I present several case studies to illustrate how theoretical linguistics and neural language models are still relevant to each other. This thesis contributes three studies that explore different aspects of the syntax-semantics interface in language models.
  arXiv  Detail & Related papers  (2022-07-20T04:20:46Z)
• Schr\"odinger's Tree -- On Syntax and Neural Language Models [10.296219074343785]
  Language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities. We observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form. We outline the implications of the different types of research questions exhibited in studies on syntax.
  arXiv  Detail & Related papers  (2021-10-17T18:25:23Z)
• The Rediscovery Hypothesis: Language Models Need to Meet Linguistics [8.293055016429863]
  We study whether linguistic knowledge is a necessary condition for good performance of modern language models. We show that language models that are significantly compressed but perform well on their pretraining objectives retain good scores when probed for linguistic structures. This result supports the rediscovery hypothesis and leads to the second contribution of our paper: an information-theoretic framework that relates language modeling objective with linguistic information.
  arXiv  Detail & Related papers  (2021-03-02T15:57:39Z)
• Language Modelling as a Multi-Task Problem [12.48699285085636]
  We investigate whether language models adhere to learning principles of multi-task learning during training. Experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.
  arXiv  Detail & Related papers  (2021-01-27T09:47:42Z)

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.