Large Linguistic Models: Analyzing theoretical linguistic abilities of LLMs

• URL: http://arxiv.org/abs/2305.00948v2
• Date: Mon, 21 Aug 2023 16:52:29 GMT
• Title: Large Linguistic Models: Analyzing theoretical linguistic abilities of LLMs
• Authors: Ga\v{s}per Begu\v{s} and Maksymilian D\k{a}bkowski and Ryan Rhodes
• Abstract summary: We show that large language models can generate coherent and valid formal analyses of linguistic data. We focus on three subfields of formal linguistics: syntax, phonology, and semantics. This line of inquiry exemplifies behavioral interpretability of deep learning, where models' representations are accessed by explicit prompting.
• Score: 7.4815059492034335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of large language models (LLMs) has recently improved to the point where the models can perform well on many language tasks. We show here that for the first time, the models can also generate coherent and valid formal analyses of linguistic data and illustrate the vast potential of large language models for analyses of their metalinguistic abilities. LLMs are primarily trained on language data in the form of text; analyzing and evaluating their metalinguistic abilities improves our understanding of their general capabilities and sheds new light on theoretical models in linguistics. In this paper, we probe into GPT-4's metalinguistic capabilities by focusing on three subfields of formal linguistics: syntax, phonology, and semantics. We outline a research program for metalinguistic analyses of large language models, propose experimental designs, provide general guidelines, discuss limitations, and offer future directions for this line of research. This line of inquiry also exemplifies behavioral interpretability of deep learning, where models' representations are accessed by explicit prompting rather than internal representations.

Related papers

• Linguistically Grounded Analysis of Language Models using Shapley Head Values [2.914115079173979]
  We investigate the processing of morphosyntactic phenomena by leveraging a recently proposed method for probing language models via Shapley Head Values (SHVs) Using the English language BLiMP dataset, we test our approach on two widely used models, BERT and RoBERTa, and compare how linguistic constructions are handled. Our results show that SHV-based attributions reveal distinct patterns across both models, providing insights into how language models organize and process linguistic information.
  arXiv  Detail & Related papers  (2024-10-17T09:48:08Z)
• Language Models as Models of Language [0.0]
  This chapter critically examines the potential contributions of modern language models to theoretical linguistics. I review a growing body of empirical evidence suggesting that language models can learn hierarchical syntactic structure and exhibit sensitivity to various linguistic phenomena. I conclude that closer collaboration between theoretical linguists and computational researchers could yield valuable insights.
  arXiv  Detail & Related papers  (2024-08-13T18:26:04Z)
• 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)
• Formal Aspects of Language Modeling [74.16212987886013]
  Large language models have become one of the most commonly deployed NLP inventions. These notes are the accompaniment to the theoretical portion of the ETH Z"urich course on large language models.
  arXiv  Detail & Related papers  (2023-11-07T20:21:42Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• SINC: Self-Supervised In-Context Learning for Vision-Language Tasks [64.44336003123102]
  We propose a framework to enable in-context learning in large language models. A meta-model can learn on self-supervised prompts consisting of tailored demonstrations. Experiments show that SINC outperforms gradient-based methods in various vision-language tasks.
  arXiv  Detail & Related papers  (2023-07-15T08:33:08Z)
• Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding [1.827510863075184]
  Curriculum is a new format of NLI benchmark for evaluation of broad-coverage linguistic phenomena. We show that this linguistic-phenomena-driven benchmark can serve as an effective tool for diagnosing model behavior and verifying model learning quality.
  arXiv  Detail & Related papers  (2022-04-13T10:32:03Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher [83.98181046650664]
  We present an analysis of Transformer-based language model performance across a wide range of model scales. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language. We discuss the application of language models to AI safety and the mitigation of downstream harms.
  arXiv  Detail & Related papers  (2021-12-08T19:41:47Z)
• Probing Linguistic Information For Logical Inference In Pre-trained Language Models [2.4366811507669124]
  We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations. We find that (i) pre-trained language models do encode several types of linguistic information for inference, but there are also some types of information that are weakly encoded. We have demonstrated language models' potential as semantic and background knowledge bases for supporting symbolic inference methods.
  arXiv  Detail & Related papers  (2021-12-03T07:19:42Z)
• A Survey of Knowledge Enhanced Pre-trained Models [28.160826399552462]
  We refer to pre-trained language models with knowledge injection as knowledge-enhanced pre-trained language models (KEPLMs) These models demonstrate deep understanding and logical reasoning and introduce interpretability.
  arXiv  Detail & Related papers  (2021-10-01T08:51:58Z)

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.