Papers by Julian Katz-Samuels
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs (2025.acl-long)
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Nicholas E. Corrado, Julian Katz-Samuels, Adithya M Devraj, Hyokun Yun, Chao Zhang, Yi Xu, Yi Pan, Bing Yin, Trishul Chilimbi
| Challenge: | Existing approaches to align large language models rely on large ablation studies, heuristics, or human intuition to produce models with strong performance across tasks. |
| Approach: | They propose an algorithm that mixes datasets during LLM training to balance performance across multiple tasks. |
| Outcome: | The proposed algorithm outperforms existing methods on multitask alignment setups and achieves convergence rate of O(1/T) in the convex case. |
Evolutionary Contrastive Distillation for Language Model Alignment (2024.findings-emnlp)
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Julian Katz-Samuels, Zheng Li, Hyokun Yun, Priyanka Nigam, Yi Xu, Vaclav Petricek, Bing Yin, Trishul Chilimbi
| Challenge: | Existing studies indicate that large language models struggle with challenging instructions. |
| Approach: | They propose a method for generating high-quality synthetic preference data to enhance the complex instruction-following capability of language models. |
| Outcome: | The proposed method exceeds the performance of current SOTA 7B models and is competitive even with open-source 70B models. |
InfoPO: On Mutual Information Maximization for Large Language Model Alignment (2025.naacl-long)
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Teng Xiao, Zhen Ge, Sujay Sanghavi, Tian Wang, Julian Katz-Samuels, Marc Versage, Qingjun Cui, Trishul Chilimbi
| Challenge: | Recent studies have shown that direct preference optimization and its variants can be useful for fine-tuning large language models with human preferences data. |
| Approach: | They propose a preference fine-tuning algorithm that effectively and efficiently aligns large language models using preference data. |
| Outcome: | Extensive experiments show that the proposed algorithm outperforms established baselines on reasoning tasks. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |