Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives

• URL: http://arxiv.org/abs/2607.18443v1
• Date: Mon, 20 Jul 2026 18:51:46 GMT
• Title: Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives
• Abstract summary: ScAffolded Generative models for Explanation combines the explanatory transparency of cognitive models with the generative flexibility of language models.<n>SAGE decomposes a pragmatic process into three kinds of modules: proposers, evaluators, and selectors.<n>We discuss the promise and the limitations of neuro-symbolic models as candidate explanatory accounts of human pragmatic language use.
• Score: 3.746889836344766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification. Here we propose a framework, ScAffolded Generative models for Explanation (SAGE), that combines the explanatory transparency of cognitive models with the generative flexibility of language models (LMs). SAGE decomposes a pragmatic process into three kinds of modules: proposers, which use LMs to generate an open-ended space of candidate alternatives; evaluators, which assess those alternatives (e.g., their semantics, complexity, or typicality); and selectors, which implement the rule-based computational steps of a cognitively motivated task analysis. We assess SAGE in three case studies spanning pragmatic generation and interpretation-referential expression generation, manner (M-)implicatures, and Gricean conversational implicatures. SAGE models are evaluated critically using established methods from computational cognitive modeling, including ablations, baseline comparisons, and quantitative fit to human data. Across studies, SAGE models achieved high accuracy and often outperformed baselines, but component-level analyses reveal an asymmetry: LM proposers reliably generated alternatives well-suited to pragmatic modeling, whereas LM evaluators are better at providing intuitive judgements rather than judgements of theoretical or formal measures. We discuss the promise and the limitations of neuro-symbolic models as candidate explanatory accounts of human pragmatic language use.

Related papers

• Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
  We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
  arXiv  Detail & Related papers  (2025-07-16T18:01:03Z)
• Using cognitive models to reveal value trade-offs in language models [12.178109894945981]
  We use a cognitive model of polite speech to evaluate value trade-offs in two encompassing model settings.<n>Our results highlight patterns of higher informational utility than social utility in reasoning models' default behavior.<n>Our framework offers a flexible tool for probing value trade-offs across diverse model types.
  arXiv  Detail & Related papers  (2025-06-25T17:58:12Z)
• Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering [9.043409663314419]
  We propose a neuro-symbolic framework that enhances probabilistic cognitive models.<n>We examine various approaches to incorporating neural modules into the cognitive model.<n>We find that hybrid models can match or exceed the performance of traditional probabilistic models in predicting human answer patterns.
  arXiv  Detail & Related papers  (2025-06-02T09:34:37Z)
• The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think [81.38614558541772]
  We introduce the CoT Encyclopedia, a framework for analyzing and steering model reasoning.<n>Our method automatically extracts diverse reasoning criteria from model-generated CoTs.<n>We show that this framework produces more interpretable and comprehensive analyses than existing methods.
  arXiv  Detail & Related papers  (2025-05-15T11:31:02Z)
• LatentQA: Teaching LLMs to Decode Activations Into Natural Language [72.87064562349742]
  We introduce LatentQA, the task of answering open-ended questions about model activations in natural language.<n>We propose Latent Interpretation Tuning (LIT), which finetunes a decoder LLM on a dataset of activations and associated question-answer pairs.<n>Our decoder also specifies a differentiable loss that we use to control models, such as debiasing models on stereotyped sentences and controlling the sentiment of generations.
  arXiv  Detail & Related papers  (2024-12-11T18:59:33Z)
• Interpret the Internal States of Recommendation Model with Sparse Autoencoder [28.234859617081295]
  RecSAE is an automated and generalizable probing framework that interprets Recommenders with Sparse AutoEncoder.<n>It extracts interpretable latents from the internal states of recommendation models and links them to semantic concepts for interpretation.<n> RecSAE does not alter original models during interpretation and also enables targeted de-biasing to models based on interpreted results.
  arXiv  Detail & Related papers  (2024-11-09T08:22:31Z)
• Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
  We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance.<n>This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation.<n>Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
  arXiv  Detail & Related papers  (2024-07-07T17:05:24Z)
• Modeling Boundedly Rational Agents with Latent Inference Budgets [56.24971011281947]
  We introduce a latent inference budget model (L-IBM) that models agents' computational constraints explicitly. L-IBMs make it possible to learn agent models using data from diverse populations of suboptimal actors. We show that L-IBMs match or outperform Boltzmann models of decision-making under uncertainty.
  arXiv  Detail & Related papers  (2023-12-07T03:55:51Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• RecExplainer: Aligning Large Language Models for Explaining Recommendation Models [50.74181089742969]
  Large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explain black-box recommender models. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment.
  arXiv  Detail & Related papers  (2023-11-18T03:05:43Z)
• Pseudointelligence: A Unifying Framework for Language Model Evaluation [14.95543156914676]
  We propose a complexity-theoretic framework of model evaluation cast as a dynamic interaction between a model and a learned evaluator. We demonstrate that this framework can be used to reason about two case studies in language model evaluation, as well as analyze existing evaluation methods.
  arXiv  Detail & Related papers  (2023-10-18T17:48:05Z)
• Evaluating Language Models for Mathematics through Interactions [116.67206980096513]
  We introduce CheckMate, a prototype platform for humans to interact with and evaluate large language models (LLMs) We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics. We derive a taxonomy of human behaviours and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness.
  arXiv  Detail & Related papers  (2023-06-02T17:12:25Z)
• Model-Based Counterfactual Synthesizer for Interpretation [40.01787107375103]
  We propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models. We first analyze the model-based counterfactual process and construct a base synthesizer using a conditional generative adversarial net (CGAN) To better approximate the counterfactual universe for those rare queries, we novelly employ the umbrella sampling technique to conduct the MCS framework training.
  arXiv  Detail & Related papers  (2021-06-16T17:09:57Z)

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.