3 citations · 9 across the 13 of their papers we have counts for
13 papers
Uncovering the Computational Ingredients of Human-Like Representations in LLMs
Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee +1
The human ability to translate diverse perceptual and linguistic inputs into structured behavior has been thought to rest on learning robust representations of concepts. The rapid…
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…
AI-enhanced semantic feature norms for 786 concepts
Siddharth Suresh, Kushin Mukherjee, Tyler Giallanza +4
Semantic feature norms have been foundational in the study of human conceptual knowledge, yet traditional methods face trade-offs between concept/feature coverage and verifiability…
Evaluating Steering Techniques using Human Similarity Judgments
Zach Studdiford, Timothy T. Rogers, Siddharth Suresh +1
Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representations align with human cognition. U…
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…
Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds Decoding
Yun-Shiuan Chuang, Sameer Narendran, Nikunj Harlalka +5
Guesstimation -- the task of making approximate quantitative estimates about objects or events -- is a common real-world skill, yet remains underexplored in large language model (L…
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
Jifan Zhang, Lalit Jain, Yang Guo +9
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing…
Learning interactions to boost human creativity with bandits and GPT-4
Ara Vartanian, Xiaoxi Sun, Yun-Shiuan Chuang +3
This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely seman…
The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents
Yun-Shiuan Chuang, Siddharth Suresh, Nikunj Harlalka +6
Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of part…
Simulating Opinion Dynamics with Networks of LLM-based Agents
Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka +6
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the age…
Semantic Feature Verification in FLAN-T5
Siddharth Suresh, Kushin Mukherjee, Timothy T. Rogers
This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive s…
Human-machine cooperation for semantic feature listing
Kushin Mukherjee, Siddharth Suresh, Timothy T. Rogers
Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human…
Conceptual structure coheres in human cognition but not in large language models
Siddharth Suresh, Kushin Mukherjee, Xizheng Yu +3
Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved e…