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Ziwen Han

5 accepted papers

2026

MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes

Yu Ying Chiu, Michael S. Lee, Rachel Calcott, Brandon Handoko, Paul de Font-Reaulx, Paula Rodriguez +12
ICLR 2026poster

As AI systems progresses, we rely more on them to make decisions with us and for us. To ensure that such decisions are aligned with human values, it is imperative for us to understand not only what decisions they make but also how they come to those decisions. Reasoning language models, which provid…

Cited by 0Source ↗code ↗Scholar ↗
2025

Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses

David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot
ICLR 2025poster

Vulnerability of Frontier language models to misuse has prompted the development of safety measures like filters and alignment training seeking to ensure safety through robustness to adversarially crafted prompts. We assert that robustness is fundamentally insufficient for ensuring safety goals due…

Cited by 2Source ↗PDF ↗Scholar ↗
2025

Planning in Natural Language Improves LLM Search for Code Generation

Evan Z Wang, Federico Cassano, Catherine Wu, Yunfeng Bai, William Song, Vaskar Nath +4
ICLR 2025spotlight

While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute only recently began to yield analogous gains. We hypothesize that a core missing component is a lack of diverse LLM outputs, leading to inefficient search due to models repeat…

Cited by 27Source ↗PDF ↗code ↗Scholar ↗
2025

Teaching LLMs How to Learn with Contextual Fine-Tuning

Younwoo Choi, Muhammad Adil Asif, Ziwen Han, John Willes, Rahul Krishnan
ICLR 2025poster

Prompting Large Language Models (LLMs), or providing context on the expected model of operation, is an effective way to steer the outputs of such models to satisfy human desiderata after they have been trained. But in rapidly evolving domains, there is often need to fine-tune LLMs to improve either…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Large Language Models are Human-Level Prompt Engineers

Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan +1
ICLR 2023poster

By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted b…

Cited by 1085Source ↗PDF ↗code ↗Scholar ↗