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Eduard Hovy

ACL 2026×2COLM 2026ICML 2026可解释性评测与基准对齐与偏好强化学习与后训练效率与系统
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0 一作 · 4 篇 · ACL 2026 / COLM 2026 / ICML 2026

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2026 顶会论文

4
作者位次 #3/4COLM 2026Poster· Poster Session 3· Imperial Ballroom· #52

Measuring Pragmatic Influence in LLM Instructions

Yilin Geng · Omri Abend · Eduard Hovy · Lea Frermann

We introduce the first controlled framework for measuring how pragmatic framing shifts LLM instruction-following behavior, showing that susceptibility is a structured, cross-model property of instruction-tuned systems.

摘要

It is not only _what_ we ask large language models (LLMs) to do that matters, but also _how_ we ask them. Phrases like "This is urgent" or "As your supervisor" can shift model behavior without altering task content. We study this effect as **pragmatic framing**, contextual cues that shape directive interpretation rather than task specification. While prior work exploits such cues for prompt optimization or probes them as security vulnerabilities, pragmatic framing itself has received comparatively little attention as a target of controlled measurement in instruction following. To support its systematic study as a measurable property, we introduce a framework that combines three components: directive-framing decomposition separating framing context from task specification; a taxonomy organizing 400 instantiations of framing into 13 strategies across 4 mechanism clusters; and priority-based measurement that quantifies influence through observable shifts in directive prioritization. Evaluating five open-weight LLMs across different families and scales, we find that pragmatic framing produces systematic shifts in directive prioritization, and the effectiveness ranking of different strategies proves highly consistent across models. This reveals that susceptibility to pragmatic framing is a structured behavioral property of instruction-tuned systems. Measuring this susceptibility is a prerequisite for any deliberate response to it, and this work provides the framework to do so.

可解释性评测与基准对齐与偏好LLMpragmatic framingpersuasioninstruction followingOpenReview
作者位次 #3/3ICML 2026Poster· Deep Learning / Foundation Models

When Is Rank-1 Enough? Geometry-Guided Initialization for Parameter-Efficient Fine-Tuning

Haoran Zhao · Caren Han · Eduard Hovy

强化学习与后训练效率与系统OpenReview
作者位次 #6/6ACL 2026Poster· Natural Language Processing

EVOTOOL : Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection

Shuo Yang · Soyeon Caren Han · Xueqi Ma · Yan Li · Mohammad Reza Ghasemi Madani · Eduard Hovy

摘要

LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging dueto delayed supervision and the difficulty ofcredit assignment in long-horizon trajectories.Existing optimization approaches tend to beeither monolithic, which are pr...

智能体与工具可解释性多模态OpenReview 项目页
作者位次 #3/4ACL 2026Findings· Findings of ACL

Article and Comment Frames Shape the Quality of Online Comments

Matteo Guida · Yulia Otmakhova · Eduard Hovy · Lea Frermann

摘要

Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. While recent work has shown that article framing systematically shapes the content of reader responses, this paper asks: does framing also affect respo...

社会与人本理论与学习动力学OpenReview 项目页

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