Improving Cooperation in Language Games with Bayesian Inference and the Cognitive Hierarchy

• URL: http://arxiv.org/abs/2412.12409v1
• Date: Mon, 16 Dec 2024 23:24:12 GMT
• Title: Improving Cooperation in Language Games with Bayesian Inference and the Cognitive Hierarchy
• Authors: Joseph Bills, Christopher Archibald, Diego Blaylock,
• Abstract summary: In language games, failure may be due to disagreement in the understanding of either the semantics or pragmatics of an utterance. We model coarse uncertainty in semantics using a prior distribution of language models and uncertainty in pragmatics using the cognitive hierarchy. To handle all forms of uncertainty we construct agents that learn the behavior of their partner using Bayesian inference.
• Score: 0.8149787238021642
• License:
• Abstract: In two-player cooperative games, agents can play together effectively when they have accurate assumptions about how their teammate will behave, but may perform poorly when these assumptions are inaccurate. In language games, failure may be due to disagreement in the understanding of either the semantics or pragmatics of an utterance. We model coarse uncertainty in semantics using a prior distribution of language models and uncertainty in pragmatics using the cognitive hierarchy, combining the two aspects into a single prior distribution over possible partner types. Fine-grained uncertainty in semantics is modeled using noise that is added to the embeddings of words in the language. To handle all forms of uncertainty we construct agents that learn the behavior of their partner using Bayesian inference and use this information to maximize the expected value of a heuristic function. We test this approach by constructing Bayesian agents for the game of Codenames, and show that they perform better in experiments where semantics is uncertain

Related papers

• Gaussian Mixture Models for Affordance Learning using Bayesian Networks [50.18477618198277]
  Affordances are fundamental descriptors of relationships between actions, objects and effects. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences.
  arXiv  Detail & Related papers  (2024-02-08T22:05:45Z)
• Regularized Conventions: Equilibrium Computation as a Model of Pragmatic Reasoning [72.21876989058858]
  We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games. In this model speakers and listeners search for contextually appropriate utterance--meaning mappings that are both close to game-theoretically optimal conventions and close to a shared, ''default'' semantics.
  arXiv  Detail & Related papers  (2023-11-16T09:42:36Z)
• Improving Language Models Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary [65.268245109828]
  Non-human-like behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness. A striking phenomenon is the generation of inconsistent predictions, which produces contradictory results. We propose a practical approach that alleviates the inconsistent behaviour issue by improving PLM awareness.
  arXiv  Detail & Related papers  (2023-10-24T06:15:15Z)
• Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
  The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
  arXiv  Detail & Related papers  (2023-07-06T10:52:22Z)
• Modeling Cross-Cultural Pragmatic Inference with Codenames Duet [40.52354928048333]
  This paper introduces the Cultural Codes dataset, which operationalizes sociocultural pragmatic inference in a simple word reference game. Our dataset consists of 794 games with 7,703 turns, distributed across 153 unique players. Our experiments show that accounting for background characteristics significantly improves model performance for tasks related to clue giving and guessing.
  arXiv  Detail & Related papers  (2023-06-04T20:47:07Z)
• Modelling Commonsense Properties using Pre-Trained Bi-Encoders [40.327695801431375]
  We study the possibility of fine-tuning language models to explicitly model concepts and their properties. Our experimental results show that the resulting encoders allow us to predict commonsense properties with much higher accuracy than is possible.
  arXiv  Detail & Related papers  (2022-10-06T09:17:34Z)
• 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)
• Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models [32.003787396501075]
  ParaBART is a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax.
  arXiv  Detail & Related papers  (2021-04-11T21:34:46Z)
• Speakers Fill Lexical Semantic Gaps with Context [65.08205006886591]
  We operationalise the lexical ambiguity of a word as the entropy of meanings it can take. We find significant correlations between our estimate of ambiguity and the number of synonyms a word has in WordNet. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
  arXiv  Detail & Related papers  (2020-10-05T17:19:10Z)
• Incorporating Pragmatic Reasoning Communication into Emergent Language [38.134221799334426]
  We study the dynamics of linguistic communication along substantially different intelligence and intelligence levels. We propose computational models that combine short-term mutual reasoning-based pragmatics with long-term language emergentism. Our results shed light on their importance for making inroads towards getting more natural, accurate, robust, fine-grained, and succinct utterances.
  arXiv  Detail & Related papers  (2020-06-07T10:31:06Z)

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.