Learning the Latent Rules of a Game from Data: A Chess Story

• URL: http://arxiv.org/abs/2410.02426v1
• Date: Thu, 3 Oct 2024 12:19:49 GMT
• Title: Learning the Latent Rules of a Game from Data: A Chess Story
• Authors: Ben Fauber,
• Abstract summary: We show that 28M and 125M parameter pretrained small language models (SLMs) can be instruction fine-tuned with 1,000-to-1,000,000 examples. We also explore the impact of successive language model fine-tuning epochs on improved outcomes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate that small pretrained foundational generative language models with millions of parameters can learn the latent rules of a process from data associated with the process. Inspired by Stefan Zweig's novella "Schachnovelle," also known as "The Royal Game" in English, we show that 28M and 125M parameter pretrained foundational small language models (SLMs) can be instruction fine-tuned with 1,000-to-1,000,000 examples to learn the rules of chess, propose legal moves, and accurately solve chess problems. We also explore the impact of successive language model fine-tuning epochs on improved outcomes and demonstrate reductions in model hallucinations by increasing the number of instruction fine-tuning examples.

Related papers

• Cross-model Control: Improving Multiple Large Language Models in One-time Training [34.98931804630706]
  Cross-model Control (CMC) is a method that improves multiple large language models in one-time training. Based on this insight, we incorporate a tiny language model with a minimal number of parameters. We propose a novel token mapping strategy named PM-MinED to make this tiny language model applicable to models with different vocabularies.
  arXiv  Detail & Related papers  (2024-10-23T06:52:09Z)
• Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks [0.0]
  We propose that small pretrained foundational generative language models can be utilized as a general learning framework for sequence-based tasks. Our proposal overcomes the computational resource, skill set, and timeline challenges associated with training neural networks and language models from scratch. We demonstrate that 125M, 350M, and 1.3B parameter pretrained foundational language models can be instruction fine-tuned with 10,000-to-1,000,000 instruction examples.
  arXiv  Detail & Related papers  (2024-02-08T12:19:32Z)
• TIM: Teaching Large Language Models to Translate with Comparison [78.66926087162672]
  We propose a novel framework using examples in comparison to teach LLMs to learn translation. Our approach involves presenting the model with examples of correct and incorrect translations and using a preference loss to guide the model's learning. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations.
  arXiv  Detail & Related papers  (2023-07-10T08:15:40Z)
• Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective [26.41585967095811]
  Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training. Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN. Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification.
  arXiv  Detail & Related papers  (2022-10-16T17:24:06Z)
• Learning Chess With Language Models and Transformers [0.0]
  Representing a board game and its positions by text-based notation enables the possibility of NLP applications. BERT models, first to the simple Nim game to analyze its performance in the presence of noise in a setup of a few-shot learning architecture. Model practically learns the rules of the chess game and can survive games against Stockfish at a category-A rating level.
  arXiv  Detail & Related papers  (2022-09-24T01:22:59Z)
• PaLM: Scaling Language Modeling with Pathways [180.69584031908113]
  We trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.
  arXiv  Detail & Related papers  (2022-04-05T16:11:45Z)
• Learning Symbolic Rules for Reasoning in Quasi-Natural Language [74.96601852906328]
  We build a rule-based system that can reason with natural language input but without the manual construction of rules. We propose MetaQNL, a "Quasi-Natural" language that can express both formal logic and natural language sentences. Our approach achieves state-of-the-art accuracy on multiple reasoning benchmarks.
  arXiv  Detail & Related papers  (2021-11-23T17:49:00Z)
• Finetuned Language Models Are Zero-Shot Learners [67.70352207685558]
  We show that instruction tuning boosts zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types.
  arXiv  Detail & Related papers  (2021-09-03T17:55:52Z)
• Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models [62.41139712595334]
  We propose a novel pre-training paradigm for Chinese -- Lattice-BERT. We construct a lattice graph from the characters and words in a sentence and feed all these text units into transformers. We show that our model can bring an average increase of 1.5% under the 12-layer setting.
  arXiv  Detail & Related papers  (2021-04-15T02:36:49Z)
• Learning Chess Blindfolded: Evaluating Language Models on State Tracking [69.3794549747725]
  We consider the task of language modeling for the game of chess. Unlike natural language, chess notations describe a simple, constrained, and deterministic domain. We find that transformer language models can learn to track pieces and predict legal moves with high accuracy when trained solely on move sequences.
  arXiv  Detail & Related papers  (2021-02-26T01:16:23Z)

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.