Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction

• URL: http://arxiv.org/abs/2510.25426v1
• Date: Wed, 29 Oct 2025 11:49:42 GMT
• Title: Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction
• Authors: Asutosh Hota, Jussi P. P. Jokinen,
• Abstract summary: Implicature (meaning conveyed beyond explicit statements through shared context) is essential for human-AI alignment.<n>This study examines Large Language Models' ability to infer user intent embedded in context-driven prompts.<n>Results show that larger models approximate human interpretations more closely, while smaller models struggle with implicature inference.
• Score: 8.735329612895578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of Large Language Models (LLMs) is positioning language at the core of human-computer interaction (HCI). We argue that advancing HCI requires attention to the linguistic foundations of interaction, particularly implicature (meaning conveyed beyond explicit statements through shared context) which is essential for human-AI (HAI) alignment. This study examines LLMs' ability to infer user intent embedded in context-driven prompts and whether understanding implicature improves response generation. Results show that larger models approximate human interpretations more closely, while smaller models struggle with implicature inference. Furthermore, implicature-based prompts significantly enhance the perceived relevance and quality of responses across models, with notable gains in smaller models. Overall, 67.6% of participants preferred responses with implicature-embedded prompts to literal ones, highlighting a clear preference for contextually nuanced communication. Our work contributes to understanding how linguistic theory can be used to address the alignment problem by making HAI interaction more natural and contextually grounded.

Related papers

• Generative Human-Object Interaction Detection via Differentiable Cognitive Steering of Multi-modal LLMs [85.69785384599827]
  Human-object interaction (HOI) detection aims to localize human-object pairs and the interactions between them.<n>Existing methods operate under a closed-world assumption, treating the task as a classification problem over a small, predefined verb set.<n>We propose GRASP-HO, a novel Generative Reasoning And Steerable Perception framework that reformulates HOI detection from the closed-set classification task to the open-vocabulary generation problem.
  arXiv  Detail & Related papers  (2025-12-19T14:41:50Z)
• How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction [6.649393350057383]
  This study systematically investigates the structural and behavioral differences between AI-simulated and authentic human tutoring dialogues.<n>Results show that human dialogues are significantly superior to their AI counterparts in utterance length, as well as in questioning (I-Q) and general feedback (F-F) behaviors.
  arXiv  Detail & Related papers  (2025-09-02T03:18:39Z)
• Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding [0.0]
  The paper discusses state-of-the-art methodologies that advance large language models (LLMs) with more advanced NLU techniques.<n>We analyze the use of structured knowledge graphs, retrieval-augmented generation (RAG), and fine-tuning strategies that match models with human-level understanding.
  arXiv  Detail & Related papers  (2025-04-01T04:12:04Z)
• VAGUE: Visual Contexts Clarify Ambiguous Expressions [26.190504250419547]
  VAGUE is a benchmark evaluating multimodal AI systems' ability to integrate visual context for intent.<n>Our experiments reveal that existing multimodal AI models struggle to infer the speaker's true intent.<n>Analysis of failure cases demonstrates that current models fail to distinguish true intent from superficial correlations in the visual scene.
  arXiv  Detail & Related papers  (2024-11-21T14:01:42Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models [55.20626448358655]
  This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs) Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence.
  arXiv  Detail & Related papers  (2023-11-07T08:27:32Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors [36.75629570208193]
  Human-object interaction (HOI) detection aims to extract interacting human-object pairs and their interaction categories from a given natural image. In this paper, we tackle HOI detection with the weakest supervision setting in the literature, using only image-level interaction labels. We first propose an approach to prune non-interacting human and object proposals to increase the quality of positive pairs within the bag, exploiting the grounding capability of the vision-language model. Second, we use a large language model to query which interactions are possible between a human and a given object category, in order to force the model not to put emphasis
  arXiv  Detail & Related papers  (2023-03-09T19:08:02Z)
• Interpretable Artificial Intelligence through the Lens of Feature Interaction [11.217688723644454]
  This work first explains the historical and modern importance of feature interactions and then surveys the modern interpretability methods which do explicitly consider feature interactions. This survey aims to bring to light the importance of feature interactions in the larger context of machine learning interpretability.
  arXiv  Detail & Related papers  (2021-03-01T23:23:10Z)

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.