3 citations · 9 across the 6 of their papers we have counts for
13 papers · 1 filter
LLMs Can Better Capture Human Judgments--With the Right Prompts
Danica Dillion, Chen Cecilia Liu, Baihui Wang +5
Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judg…
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data
Tao Feng, Lizhen Qu, Niket Tandon +1
Causal discovery is fundamental to scientific research, yet traditional statistical algorithms face significant challenges, including expensive data collection, redundant computati…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
On the Reliability of Large Language Models for Causal Discovery
Tao Feng, Lizhen Qu, Niket Tandon +3
This study investigates the efficacy of Large Language Models (LLMs) in causal discovery. Using newly available open-source LLMs, OLMo and BLOOM, which provide access to their pre-…
PDDLEGO: Iterative Planning in Textual Environments
Li Zhang, Peter Jansen, Tianyi Zhang +3
Planning in textual environments have been shown to be a long-standing challenge even for current models. A recent, promising line of work uses LLMs to generate a formal representa…
WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models
Wenlong Zhao, Debanjan Mondal, Niket Tandon +3
The awareness of multi-cultural human values is critical to the ability of language models (LMs) to generate safe and personalized responses. However, this awareness of LMs has bee…