惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

雷峰网
雷峰网
IT之家
IT之家
Last Week in AI
Last Week in AI
J
Java Code Geeks
L
LangChain Blog
Recent Announcements
Recent Announcements
Martin Fowler
Martin Fowler
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Google DeepMind News
Google DeepMind News
博客园 - Franky
博客园 - 司徒正美
月光博客
月光博客
博客园 - 叶小钗
Vercel News
Vercel News
腾讯CDC
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
小众软件
小众软件
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
GbyAI
GbyAI
B
Blog RSS Feed
人人都是产品经理
人人都是产品经理
H
Help Net Security
G
Google Developers Blog
D
DataBreaches.Net

Paper Index on ACL Anthology

A Bounded Coordination-Support Capability for Multi-Party Settings: Task-State Monitoring in Firefighter Incident Command A Dataset of Latin Etymologies Extracted from Wiktionary An Efficient Approach for Answering Not Readily Attainable Questions for RAG-based Applications Automated German Alt Text Generation for News Charts Call Support Copilot: A Reproducible Multimodal System for Speech Emotion Recognition, Intent Understanding, and Agent Assistance Can Large Language Models Replace Statistical Software? Code-Switching Detection in Multilingual Child Speech with SwissBERT Concept Extraction and Webb’s Depth of Knowledge: Comparing LLM Question Generation Pipelines for Educational Assessment Data Augmentation for Historical NER: A Systematic Comparison of Lexical and LLM-based Approaches Enhancing Retrieval via Cognitively Motivated Document Expansion Extending the Contact Hypothesis: Cross-Linguistic Evaluation of Religion and Nationality Bias When Prompting LLMs in German and Icelandic Extracting Article-Level Legal Dependencies from Swiss Federal Law using LLMs How Good is AI on Swiss Voting Booklets? A Multilingual OCR and Alignment Benchmark Optimizing Large Language Models for Robust Domain-Specific Text-to-SQL: From Prompting to Preference Alignment Proceedings of the 11th Edition of the Swiss Text Analytics Conference Reinforcement Learning for Latent-Space Thinking in LLMs RUMLEM: A Dictionary-Based Lemmatizer for Romansh Skill Extraction from Resumes and Job Offers across Six Languages Text vs. Phoneme Intermediates for Low-Resource Swiss German The Same Email, Signed Differently: Testing Negotiation Bias and Recommendation Stability in LLMs Which Skills Debate Reaches the Public? Comparing Scientific Literature and Media Coverage of AI and LLM Skill Impacts (2022–2025) Controlling Language and Style of Multi-lingual Generative Language Models with Control Vectors Hybrid Human-LLM Corpus Construction and LLM Evaluation for the Caused-Motion Construction Implicit and Indirect: Detecting Face-threatening and Paired Actions in Asynchronous Online Conversations Northern European Journal of Language Technology, Volume 11 A modular architecture for creating multimodal embodied agents with an episodic Knowledge Graph as an explainable and controllable long-term memory A Neural Approach to Discourse Relation Signal Detection An Analysis of Japanese Sentence-final Particle Yone: Compare Yone and Ne in Response Attribution and the discourse structure of reports Automatic Detection of the Bulgarian Evidential Renarrative
Strategic Dialogue Assessment: The Crooked Path to Innocence
2026-04-20 · via Paper Index on ACL Anthology

Abstract

Language is often used strategically, particularly in high-stakes, adversarial settings, yet most work on pragmatics and LLMs centers on cooperative settings. This leaves a gap in the systematic understanding of strategic communication in adversarial settings. To address this, we introduce SDA (Strategic Dialogue Assessment), a framework grounded in Gricean and game-theoretic pragmatics to assess strategic use of language. It adapts the ME Game jury function to make it empirically estimable for analyzing dialogue. Our approach incorporates two key adaptations: a commitment-based taxonomy of discourse moves, which provides a finer-grained account of strategic effects, and the use of estimable proxies grounded in Gricean maxims to operationalize abstract constructs such as credibility. Together, these adaptations build on discourse theory by treating discourse as the strategic management of commitments, enabling systematic evaluation of how conversational moves advance or undermine discourse goals. We further derive three interpretable metrics - Benefit at Turn (BAT), Penalty at Turn (PAT), and Normalized Relative Benefit at Turn (NRBAT) - to quantify the perceived strategic effects of discourse moves. We also present CPD (the Crooked Path Dataset), an annotated dataset of real courtroom cross-examinations, to demonstrate the framework’s effectiveness. Using these tools, we evaluate a range of LLMs and show that LLMs generally exhibit limited pragmatic understanding of strategic language. While model size shows an increase in performance on our metrics, reasoning ability does not help and largely hurts, introducing overcomplication and internal confusion.

Anthology ID:
2026.dnd-17.1
Volume:
Dialogue & Discourse Volume 17
Month:
January
Year:
2026
Address:
Chicago, Illinois, USA
Editors:
Pierre Lison, Amir Zeldes
Venue:
DND
SIG:
SIGDIAL
Publisher:
University of Illinois Chicago
Note:
Pages:
1–53
Language:
URL:
https://aclanthology.org/2026.dnd-17.1/
DOI:
10.5210/dad.2026.101
Bibkey:
Cite (ACL):
Anshun Asher Zheng, Junyi Jessy Li, and David I. Beaver. 2026. Strategic Dialogue Assessment: The Crooked Path to Innocence. Dialogue & Discourse, 17:1–53.
Cite (Informal):
Strategic Dialogue Assessment: The Crooked Path to Innocence (Zheng et al., DND 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.dnd-17.1.pdf