5 citations · 17 across the 23 of their papers we have counts for
6 papers · 1 filter
Stress-Testing Model Specs Reveals Character Differences among Language Models
Jifan Zhang, Henry Sleight, Andi Peng +2
Large language models (LLMs) are increasingly trained from AI constitutions and model specifications that establish behavioral guidelines and ethical principles. However, these spe…
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs
Abhinav Arabelly, Jagrut Nemade, Robert D Nowak +1
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, but developing high-performing models for specialized applications often requires sub…
Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench
Reuben Narad, Siddharth Suresh, Jiayi Chen +5
We present HumorBench, a benchmark designed to evaluate large language models' (LLMs) ability to reason about and explain sophisticated humor in cartoon captions. As reasoning mode…
Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs
Kuan Lok Zhou, Jiayi Chen, Siddharth Suresh +6
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 Cart…
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
Jifan Zhang, Ziyue Luo, Jia Liu +2
Large language models require vast amounts of high-quality training data, but effective filtering of web-scale datasets remains a significant challenge. This paper demonstrates tha…
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
Gantavya Bhatt, Yifang Chen, Arnav M. Das +9
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language mo…