Navigating Human Language Models with Synthetic Agents

• URL: http://arxiv.org/abs/2008.04162v7
• Date: Tue, 29 Sep 2020 09:57:33 GMT
• Title: Navigating Human Language Models with Synthetic Agents
• Authors: Philip Feldman and Antonio Bucchiarone
• Abstract summary: We train a version of the GPT-2 on a corpora of historical chess games, and then "launch" clusters of synthetic agents into the model. We find that the percentages of moves by piece using the model are substantially similar from human patterns.
• Score: 7.99536002595393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently testable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then "launch" clusters of synthetic agents into the model, using text strings to create context and orientation. We compare the trajectories contained in the text generated by the agents/model and compare that to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.

Related papers

• Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models [0.0]
  We train a GPT model on Othello games and find that the model learned an internal representation of the board state. We extend this work into the more complex domain of chess, training on real games and investigating our model's internal representations. Unlike Li et al.'s prior synthetic dataset approach, our analysis finds that the model also learns to estimate latent variables like player skill to better predict the next character.
  arXiv  Detail & Related papers  (2024-03-21T18:53:23Z)
• Few-Shot Detection of Machine-Generated Text using Style Representations [4.326503887981912]
  Language models that convincingly mimic human writing pose a significant risk of abuse. We propose to leverage representations of writing style estimated from human-authored text. We find that features effective at distinguishing among human authors are also effective at distinguishing human from machine authors.
  arXiv  Detail & Related papers  (2024-01-12T17:26:51Z)
• ChessGPT: Bridging Policy Learning and Language Modeling [17.85415939196955]
  ChessGPT is a GPT model bridging policy learning and language modeling. We build a large-scale game and language dataset related to chess. We showcase two model examples ChessCLIP and ChessGPT, integrating policy learning and language modeling.
  arXiv  Detail & Related papers  (2023-06-15T15:35:31Z)
• Infusing Commonsense World Models with Graph Knowledge [89.27044249858332]
  We study the setting of generating narratives in an open world text adventure game. A graph representation of the underlying game state can be used to train models that consume and output both grounded graph representations and natural language descriptions and actions.
  arXiv  Detail & Related papers  (2023-01-13T19:58:27Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)
• Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning [53.92465205531759]
  Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. We train a contrastive bi-encoder model to align stories with human critiques, building a general purpose preference model. We further fine-tune the contrastive reward model using a prompt-learning technique to increase story generation robustness.
  arXiv  Detail & Related papers  (2022-10-14T13:21:33Z)
• Estimating the Personality of White-Box Language Models [0.589889361990138]
  Large-scale language models, which are trained on large corpora of text, are being used in a wide range of applications everywhere. Existing research shows that these models can and do capture human biases. Many of these biases, especially those that could potentially cause harm, are being well-investigated. However, studies that infer and change human personality traits inherited by these models have been scarce or non-existent.
  arXiv  Detail & Related papers  (2022-04-25T23:53:53Z)
• DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models [73.12069620086311]
  We investigate the visual reasoning capabilities and social biases of text-to-image models. First, we measure three visual reasoning skills: object recognition, object counting, and spatial relation understanding. Second, we assess the gender and skin tone biases by measuring the gender/skin tone distribution of generated images.
  arXiv  Detail & Related papers  (2022-02-08T18:36:52Z)
• TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect [0.0]
  We investigate the feasibility of training monolingual Transformer-based language models for under represented languages. We show that the use of noisy web crawled data instead of structured data is more convenient for such non-standardized language. Our best performing TunBERT model reaches or improves the state-of-the-art in all three downstream tasks.
  arXiv  Detail & Related papers  (2021-11-25T15:49:50Z)
• 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)
• Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
  Current datasets only tell you where people are, not where they could be. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
  arXiv  Detail & Related papers  (2020-08-19T23:19:08Z)

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.