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…
Causal Representation Learning from Network Data
Jifan Zhang, Michelle M. Li, Elena Zheleva
Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional…
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…
Topology-Aware Conformal Prediction for Stream Networks
Jifan Zhang, Fangxin Wang, Zihe Song +3
Stream networks, a unique class of spatiotemporal graphs, exhibit complex directional flow constraints and evolving dependencies, making uncertainty quantification a critical yet c…
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…