Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations

• URL: http://arxiv.org/abs/2405.13828v1
• Date: Wed, 22 May 2024 16:57:02 GMT
• Title: Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations
• Authors: Ziqiao Ma, Zekun Wang, Joyce Chai,
• Abstract summary: We introduce a trial-and-demonstration (TnD) learning framework that incorporates three components: student trials, teacher demonstrations, and a reward conditioned on language competence. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal or smaller numbers of parameters. Our findings suggest that interactive language learning, with teacher demonstrations and student trials, can facilitate efficient word learning in language models.
• Score: 15.394018604836774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-interactive training paradigm, and refined pre-trained models through feedback afterward. In this work, we aim to examine how corrective feedback from interactions influences neural language acquisition from the ground up through systematically controlled experiments, assessing whether it contributes to learning efficiency in language models. We introduce a trial-and-demonstration (TnD) learning framework that incorporates three components: student trials, teacher demonstrations, and a reward conditioned on language competence at various developmental stages. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal and smaller numbers of parameters, and we highlight the significance of both trials and demonstrations. We further show that the teacher's choices of words influence students' word-specific learning efficiency, and a practice-makes-perfect effect is evident by a strong correlation between the frequency of words in trials and their respective learning curves. Our findings suggest that interactive language learning, with teacher demonstrations and student trials, can facilitate efficient word learning in language models.

Related papers

• Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models [31.006803764376475]
  Semantic and syntactic bootstrapping posit that children use their prior knowledge of one linguistic domain, say syntactic relations, to help later acquire another, such as the meanings of new words. Here, we argue that they are instead both contingent on a more general learning strategy for language acquisition: joint learning. Using a series of neural visually-grounded grammar induction models, we demonstrate that both syntactic and semantic bootstrapping effects are strongest when syntax and semantics are learnt simultaneously.
  arXiv  Detail & Related papers  (2024-06-17T18:01:06Z)
• Toward In-Context Teaching: Adapting Examples to Students' Misconceptions [54.82965010592045]
  We introduce a suite of models and evaluation methods we call AdapT. AToM is a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimize for the correctness of future beliefs. Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.
  arXiv  Detail & Related papers  (2024-05-07T17:05:27Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Visual Grounding Helps Learn Word Meanings in Low-Data Regimes [47.7950860342515]
  Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension. But to achieve these results, LMs must be trained in distinctly un-human-like ways. Do models trained more naturalistically -- with grounded supervision -- exhibit more humanlike language learning? We investigate this question in the context of word learning, a key sub-task in language acquisition.
  arXiv  Detail & Related papers  (2023-10-20T03:33:36Z)
• Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition [6.47452771256903]
  We take inspiration from how human babies acquire their first language, and developed a computational process for word acquisition through comparative learning. Motivated by cognitive findings, we generated a small dataset that enables the computation models to compare the similarities and differences of various attributes. We frame the acquisition of words as not only the information filtration process, but also as representation-symbol mapping.
  arXiv  Detail & Related papers  (2023-07-05T19:38:04Z)
• BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models [56.93604813379634]
  Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels. We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
  arXiv  Detail & Related papers  (2023-06-02T12:54:38Z)
• Computational Language Acquisition with Theory of Mind [84.2267302901888]
  We build language-learning agents equipped with Theory of Mind (ToM) and measure its effects on the learning process. We find that training speakers with a highly weighted ToM listener component leads to performance gains in our image referential game setting.
  arXiv  Detail & Related papers  (2023-03-02T18:59:46Z)
• What Artificial Neural Networks Can Tell Us About Human Language Acquisition [47.761188531404066]
  Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans.
  arXiv  Detail & Related papers  (2022-08-17T00:12:37Z)
• Word Acquisition in Neural Language Models [0.38073142980733]
  We investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words. We find that the effects of concreteness, word length, and lexical class are pointedly different in children and language models.
  arXiv  Detail & Related papers  (2021-10-05T23:26:16Z)
• A Visuospatial Dataset for Naturalistic Verb Learning [18.654373173232205]
  We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access. We use the collected data to compare several distributional semantics models for verb learning.
  arXiv  Detail & Related papers  (2020-10-28T20:47:13Z)

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.