Language Models Don't Learn the Physical Manifestation of Language

• URL: http://arxiv.org/abs/2402.11349v2
• Date: Thu, 6 Jun 2024 17:20:21 GMT
• Title: Language Models Don't Learn the Physical Manifestation of Language
• Authors: Bruce W. Lee, JaeHyuk Lim,
• Abstract summary: We argue that language-only models don't learn the physical manifestation of language. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test.
• Score: 0.3529736140137004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We argue that language-only models don't learn the physical manifestation of language. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test. These tasks highlight a fundamental gap between human linguistic understanding and the sensory-deprived linguistic understanding of LLMs. In support of our hypothesis, 1. deliberate reasoning (Chain-of-Thought), 2. few-shot examples, or 3. stronger LLM from the same model family (LLaMA 2 13B -> LLaMA 2 70B) has no significant effect on H-Test performance. We bring in the philosophical case of Mary, who learns about the world in a sensory-deprived environment as a useful conceptual framework to understand how language-only models learn about the world (Jackson, 1986). Our experiments show that some of the strongest proprietary LLMs stay near random chance baseline accuracy of 50%, highlighting the limitations of linguistic knowledge acquired in the absence of sensory experience. Our code and data are available at <github.com/brucewlee/h-test>.

Related papers

• How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition [75.11808682808065]
  This study investigates whether large language models (LLMs) exhibit similar tendencies in understanding semantic size. Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding. Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario.
  arXiv  Detail & Related papers  (2025-03-01T03:35:56Z)
• Making LLMs Reason? The Intermediate Language Problem in Neurosymbolic Approaches [49.567092222782435]
  We introduce the intermediate language problem, which is the problem of choosing a suitable formal language representation for neurosymbolic approaches. We show a maximum difference in overall-accuracy of 53.20% and 49.26% in execution-accuracy. When using the GPT4o-mini LLM we beat the state-of-the-art in overall-accuracy on the ProntoQA dataset by 21.20% and by 50.50% on the ProofWriter dataset.
  arXiv  Detail & Related papers  (2025-02-24T14:49:52Z)
• 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)
• Randomly Sampled Language Reasoning Problems Reveal Limits of LLMs [8.146860674148044]
  We attempt to measure models' language understanding capacity while circumventing the risk of dataset recall. We parameterize large families of language tasks recognized by deterministic finite automata (DFAs) We find that, even in the strikingly simple setting of 3-state DFAs, LLMs underperform un parameterized ngram models on both language recognition and synthesis tasks.
  arXiv  Detail & Related papers  (2025-01-06T07:57:51Z)
• One Mind, Many Tongues: A Deep Dive into Language-Agnostic Knowledge Neurons in Large Language Models [19.58983929459173]
  Large language models (LLMs) have learned vast amounts of factual knowledge through self-supervised pre-training on large-scale corpora. LLMs have also demonstrated excellent multilingual capabilities, which can express the learned knowledge in multiple languages.
  arXiv  Detail & Related papers  (2024-11-26T13:03:49Z)
• Large Language Models as Neurolinguistic Subjects: Discrepancy in Performance and Competence for Form and Meaning [49.60849499134362]
  This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) We introduce a neurolinguistic approach, utilizing a novel method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. We found: (1) Psycholinguistic and neurolinguistic methods reveal that language performance and competence are distinct; (2) Direct probability measurement may not accurately assess linguistic competence; and (3) Instruction tuning won't change much competence but improve performance.
  arXiv  Detail & Related papers  (2024-11-12T04:16:44Z)
• Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency [0.11510009152620666]
  We argue that claims regarding linguistic capabilities of Large Language Models (LLMs) are based on at least two unfounded assumptions. Language completeness assumes that a distinct and complete thing such as a natural language' exists. The assumption of data completeness relies on the belief that a language can be quantified and wholly captured by data.
  arXiv  Detail & Related papers  (2024-07-11T18:06:01Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Can large language models understand uncommon meanings of common words? [30.527834781076546]
  Large language models (LLMs) have shown significant advancements across diverse natural language understanding (NLU) tasks. Yet, lacking widely acknowledged testing mechanisms, answering whether LLMs are parrots or genuinely comprehend the world' remains unclear. This paper presents innovative construction of a Lexical Semantic dataset with novel evaluation metrics.
  arXiv  Detail & Related papers  (2024-05-09T12:58:22Z)
• Holmes: A Benchmark to Assess the Linguistic Competence of Language Models [59.627729608055006]
  We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence. We use computation-based probing to examine LMs' internal representations regarding distinct linguistic phenomena. As a result, we meet recent calls to disentangle LMs' linguistic competence from other cognitive abilities.
  arXiv  Detail & Related papers  (2024-04-29T17:58:36Z)
• Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models [71.93366651585275]
  Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. We propose Visualization-of-Thought (VoT) to elicit spatial reasoning of LLMs by visualizing their reasoning traces. VoT significantly enhances the spatial reasoning abilities of LLMs.
  arXiv  Detail & Related papers  (2024-04-04T17:45:08Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
  Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs. Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
  arXiv  Detail & Related papers  (2024-02-26T09:36:05Z)
• When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models [59.84769254832941]
  We propose a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp. Specifically, the cunning texts that FLUB focuses on mainly consist of the tricky, humorous, and misleading texts collected from the real internet environment. Based on FLUB, we investigate the performance of multiple representative and advanced LLMs.
  arXiv  Detail & Related papers  (2024-02-16T22:12:53Z)
• POSQA: Probe the World Models of LLMs with Size Comparisons [38.30479784257936]
  Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain. With the explosive growth of Large Language Models (LLMs) and their already ubiquitous presence in our daily lives, it is becoming increasingly necessary to verify their real-world understanding.
  arXiv  Detail & Related papers  (2023-10-20T10:05:01Z)
• Spoken Language Intelligence of Large Language Models for Language Learning [3.5924382852350902]
  We focus on evaluating the efficacy of large language models (LLMs) in the realm of education. We introduce a new multiple-choice question dataset to evaluate the effectiveness of LLMs in the aforementioned scenarios. We also investigate the influence of various prompting techniques such as zero- and few-shot method. We find that models of different sizes have good understanding of concepts in phonetics, phonology, and second language acquisition, but show limitations in reasoning for real-world problems.
  arXiv  Detail & Related papers  (2023-08-28T12:47:41Z)

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.