Papers by Robert D Nowak
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across domains . but, for challenging tasks, finetuning often requires substantial human annotations - a process that is time-consuming, labor-intensive, and expensive . |
| Approach: | They propose a method that leverages task-diversity as a principle for effective data selection. |
| Outcome: | The proposed method achieves better accuracy than training on the complete dataset (4% increase in MMLU score). |
Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs (2025.findings-emnlp)
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Kuan Lok Zhou, Jiayi Chen, Siddharth Suresh, Reuben Narad, Timothy T. Rogers, Lalit K Jain, Robert D Nowak, Bob Mankoff, Jifan Zhang
| Challenge: | Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest. |
| Approach: | They propose to decompose humor understanding into three components and improve each by enhancing visual understanding through improved annotation and utilizing LLM-generated humor reasoning and explanations. |
| Outcome: | The proposed approach achieves 82.4% accuracy in caption ranking, significantly better than the previous 67% benchmark and matches the performance of world-renowned human experts in this domain. |