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Minsuk Kahng

5 accepted papers

2026

Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models

ICLR 2026poster

The tendency of users to anthropomorphise large language models (LLMs) is of growing societal interest. Here, we present AnthroBench: a novel empirical method and tool for evaluating anthropomorphic LLM behaviours in realistic settings. Our work introduces three key advances; first, we develop a mul…

Cited by 0SourcecodeScholar
2025

LLM Attributor: Interactive Visual Attribution for LLM Generation

AAAI 2025technical

While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM ATTRIBUTOR, a Python library that provides…

2025

SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment

NeurIPS 2025poster

Large Reasoning Models (LRMs) have become powerful tools for complex problem solving, but their structured reasoning pathways can lead to unsafe outputs when exposed to harmful prompts. Existing safety alignment methods reduce harmful outputs but can degrade reasoning depth, leading to significant t…

Cited by 0SourceScholar
2021

One Explanation is Not Enough: Structured Attention Graphs for Image Classification

NeurIPS 2021poster

Attention maps are popular tools for explaining the decisions of convolutional neural networks (CNNs) for image classification. Typically, for each image of interest, a single attention map is produced, which assigns weights to pixels based on their importance to the classification. We argue that a…