Divergences between Language Models and Human Brains

• URL: http://arxiv.org/abs/2311.09308v2
• Date: Mon, 5 Feb 2024 02:21:59 GMT
• Title: Divergences between Language Models and Human Brains
• Authors: Yuchen Zhou, Emmy Liu, Graham Neubig, Michael J. Tarr, Leila Wehbe
• Abstract summary: Recent research has hinted that brain signals can be effectively predicted using internal representations of language models (LMs) We show that there are clear differences in how LMs and humans represent and use language. We identify two domains that are not captured well by LMs: social/emotional intelligence and physical commonsense.
• Score: 63.405788999891335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Do machines and humans process language in similar ways? Recent research has hinted in the affirmative, finding that brain signals can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories. Using a data-driven approach, we identify two domains that are not captured well by LMs: social/emotional intelligence and physical commonsense. We then validate these domains with human behavioral experiments and show that fine-tuning LMs on these domains can improve their alignment with human brain responses.

Related papers

• Can Language Models Learn Typologically Implausible Languages? [62.823015163987996]
  Grammatical features across human languages show intriguing correlations often attributed to learning biases in humans. We discuss how language models (LMs) allow us to better determine the role of domain-general learning biases in language universals. We test LMs on an array of highly naturalistic but counterfactual versions of the English (head-initial) and Japanese (head-final) languages.
  arXiv  Detail & Related papers  (2025-02-17T20:40:01Z)
• Large Language Models as Neurolinguistic Subjects: Identifying Internal Representations for Form and Meaning [49.60849499134362]
  This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) Traditional psycholinguistic evaluations often reflect statistical biases that may misrepresent LLMs' true linguistic capabilities. We introduce a neurolinguistic approach, utilizing a novel method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers.
  arXiv  Detail & Related papers  (2024-11-12T04:16:44Z)
• Lost in Translation: The Algorithmic Gap Between LMs and the Brain [8.799971499357499]
  Language Models (LMs) have achieved impressive performance on various linguistic tasks, but their relationship to human language processing in the brain remains unclear. This paper examines the gaps and overlaps between LMs and the brain at different levels of analysis. We discuss how insights from neuroscience, such as sparsity, modularity, internal states, and interactive learning, can inform the development of more biologically plausible language models.
  arXiv  Detail & Related papers  (2024-07-05T17:43:16Z)
• Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs [70.3132264719438]
  We aim to fill the research gap by examining how neuron activation is shared across tasks and languages. We classify neurons into four distinct categories based on their responses to a specific input across different languages. Our analysis reveals the following insights: (i) the patterns of neuron sharing are significantly affected by the characteristics of tasks and examples; (ii) neuron sharing does not fully correspond with language similarity; (iii) shared neurons play a vital role in generating responses, especially those shared across all languages.
  arXiv  Detail & Related papers  (2024-06-13T16:04:11Z)
• Do Large Language Models Mirror Cognitive Language Processing? [43.68923267228057]
  Large Language Models (LLMs) have demonstrated remarkable abilities in text comprehension and logical reasoning. Brain cognitive processing signals are typically utilized to study human language processing.
  arXiv  Detail & Related papers  (2024-02-28T03:38:20Z)
• Exploring Spatial Schema Intuitions in Large Language and Vision Models [8.944921398608063]
  We investigate whether large language models (LLMs) effectively capture implicit human intuitions about building blocks of language. Surprisingly, correlations between model outputs and human responses emerge, revealing adaptability without a tangible connection to embodied experiences. This research contributes to a nuanced understanding of the interplay between language, spatial experiences, and computations made by large language models.
  arXiv  Detail & Related papers  (2024-02-01T19:25:50Z)
• Instruction-tuning Aligns LLMs to the Human Brain [19.450164922129723]
  We investigate the effect of instruction-tuning on aligning large language models and human language processing mechanisms. We find that instruction-tuning generally enhances brain alignment, but has no similar effect on behavioral alignment. Our results suggest that the mechanisms that encode world knowledge in LLMs also improve representational alignment to the human brain.
  arXiv  Detail & Related papers  (2023-12-01T13:31:02Z)
• Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context [87.31930367845125]
  We trained a lexical language model, Glove, and a supra-lexical language model, GPT-2, on a text corpus. We then assessed to what extent these information-restricted models were able to predict the time-courses of fMRI signal of humans listening to naturalistic text. Our analyses show that, while most brain regions involved in language are sensitive to both syntactic and semantic variables, the relative magnitudes of these effects vary a lot across these regions.
  arXiv  Detail & Related papers  (2023-02-28T08:16:18Z)
• Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps [75.84770193489639]
  We examine the impact of test loss, training corpus and model architecture on the prediction of functional Magnetic Resonance Imaging timecourses of participants listening to an audiobook. We find that untrained versions of each model already explain significant amount of signal in the brain by capturing similarity in brain responses across identical words. We suggest good practices for future studies aiming at explaining the human language system using neural language models.
  arXiv  Detail & Related papers  (2022-07-07T15:37:17Z)
• Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning [32.28310581819443]
  Grounded language acquisition involves learning how language-based interactions refer to the world around them. In practice the data used for learning tends to be cleaner, clearer, and more grammatical than actual human interactions. We present a dataset of common household objects described by people using either spoken or written language.
  arXiv  Detail & Related papers  (2020-07-29T17:58:04Z)

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.