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新论文 · v1arXiv:2611.13024

GRAP: Gender Rubric-Aware Probing for Analyzing Gender Bias in LLMs

Seona Seo, Geonhee Ahn, Hyunsoo Cho, Sookyung Kim

原文摘要

As LLMs increasingly mediate everyday interactions, subtle gender biases in open-ended responses pose growing societal risks. Existing probing methods have two key limitations: they rely on fixed-format tasks, such as pronoun resolution or occupation association, that alignment-tuned models can easily handle; and they compare raw group-level rubric scores, ignoring per-dimension variance and obscuring which bias dimensions reliably distinguish gender groups. We introduce GRAP (Gender Rubric-Aware Probing), a framework that uses a lightweight logistic probe to identify the most discriminative bias dimensions from responses scored across 10 rubric dimensions, operating task-agnostically on open-ended instruction following.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.02902

LUMOS: Tracing Parametric Knowledge from Training Data to Behavioral Outputs in LLMs

Seoyeon Ye, Gayoung Kim, Jiyoung Hong, Sookyung Kim, Hyunsoo Cho

原文摘要

Current analyses of LLMs' parametric knowledge are largely output-centric, drawing conclusions about what a model knows without verifying what it was actually trained on. This leaves fundamental questions, such as whether a correct response reflects genuine generalization or rote memorization, grounded in speculation rather than evidence. To resolve these ambiguities, we introduce LUMOS, a diagnostic framework that traces knowledge across training-data exposure, internal representations, and behavioral outputs, leveraging OLMo 2 with its fully transparent training corpus. By grounding analysis in verified exposure, we reveal that models internally encode rare facts with high separability (84%) yet fail to express them behaviorally (54%), though this retrieval gap narrows with scale. Furthermore, when models are asked to self-reflect on their own answers, they perform reliably on trained content (83%) but drop to random-baseline levels (49%) on unseen content. This collapse persists even under chain-of-thought prompting, which inflates confidence signals rather than improving calibration. Collectively, these findings demonstrate that incorporating the training-data axis into LLM evaluation transforms speculative diagnoses into verifiable claims, and we advocate that this axis should be a standard component of knowledge assessment in LLMs.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.13699

MIND: AI Co-Scientist for Material Research

Geonhee Ahn, Donghyun Lee, Hayoung Doo, Jonggeol Na, Hyunsoo Cho, Sookyung Kim

原文摘要

Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental verification. We propose MIND, an LLM-driven framework for automated hypothesis validation in materials research. MIND organizes the scientific discovery process into hypothesis refinement, experimentation, and debate-based validation within a multi-agent pipeline. For experimental verification, the system integrates Machine Learning Interatomic Potentials, particularly SevenNet-Omni, enabling scalable in-silico experiments. We also provide a web-based user interface for automated hypothesis testing. The modular design allows additional experimental modules to be integrated, making the framework adaptable to broader scientific workflows. The code is available at: https://github.com/IMMS-Ewha/MIND, and a demonstration video at: https://youtu.be/lqiFe1OQzN4.

cs.MA · cs.AI · cs.CE

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arXiv ↗PDF ↗