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Xiang Ren

ICLR 2026×4ACL 2026×2推理多模态可解释性社会与人本评测与基准
新星分资深
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0 一作 · 6 篇 · ICLR 2026 / ACL 2026

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2026 顶会论文

6
作者位次 #3/3ICLR 2026Poster· Applications / Language, Speech and Dialog

Segment-Level Attribution for Selective Learning of Long Reasoning Traces

Siyuan Wang · Yanchen Liu · Xiang Ren

推理多模态OpenReview
作者位次 #3/3ICLR 2026Poster· General Machine Learning / Transfer, Multitask and Meta-learning

Function Induction and Task Generalization: An Interpretability Study with Off-by-One Addition

Qinyuan Ye · Robin Jia · Xiang Ren

可解释性理论与学习动力学OpenReview
作者位次 #2/3ICLR 2026Oral· Social Aspects / Accountability, Transparency and Interpretability

Every Language Model Has a Forgery-Resistant Signature

Matthew Finlayson · Xiang Ren · Swabha Swayamdipta

可解释性社会与人本OpenReview
作者位次 #4/6ACL 2026Poster· Natural Language Processing

Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations

Keyu He · Tejas Srinivasan · Brihi Joshi · Xiang Ren · Jesse Thomason · Swabha Swayamdipta

摘要

When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM predictions with natural language explanations can signal which model predictions are reliable. However, prior work has found that explanations can...

多模态评测与基准OpenReview 项目页
作者位次 #2/2ACL 2026Poster· Natural Language Processing

DRI n Q : Evaluating Conversational Implicature with Controlled Context Variation

Hirona Jacqueline Arai · Xiang Ren

摘要

Human conversation relies heavily on conversational implicature , in which speakers convey meanings that are suggested rather than explicitly stated. Although recent large language models (LLMs) exhibit strong conversational fluency, they remain unreliable when interpretation depends on reasoning...

评测与基准社会与人本推理OpenReview 项目页

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