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François Portet

ACL 2026×2ICML 2026可解释性社会与人本智能体与工具评测与基准安全
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0 一作 · 3 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

3
作者位次 #2/3ICML 2026Poster· Social Aspects / Accountability, Transparency, and Interpretability

Mechanistic Interpretability as Statistical Estimation: A Variance Analysis

Maxime Méloux · François Portet · Maxime Peyrard

可解释性社会与人本OpenReview
作者位次 #3/4ACL 2026Findings· Findings of ACL

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM -Guided Evolutionary Search

Xinhao Zhang · Xi Chen · François Portet · Maxime Peyrard

摘要

Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary s...

智能体与工具可解释性评测与基准OpenReview 项目页
作者位次 #4/4ACL 2026Findings· Findings of ACL

Identifying and typifying demographic unfairness in phoneme-level embeddings of self-supervised speech recognition models

Felix Herron · Solange Rossato · Alexandre Allauzen · François Portet

摘要

Modern automatic speech recognition (ASR) systems have been observed to function better for certain speaker groups (SGs) than others, despite recent gains in overall performance. One potential impediment to progress towards fairer ASR is a more nuanced understanding of the types of modeling error...

社会与人本安全可解释性OpenReview 项目页

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