activity
20172025
most citedAutomatic Detection of Fake News

379 citations · 449 across the 28 of their papers we have counts for

collaborators
Showing cs.CLShow all

55 papers · 1 filter

cs.CL2025

ISCA: A Framework for Interview-Style Conversational Agents

Charles Welch, Allison Lahnala, Vasudha Varadarajan +4

We present a low-compute non-generative system for implementing interview-style conversational agents which can be used to facilitate qualitative data collection through controlled…

cs.CL2024

Language Model Alignment in Multilingual Trolley Problems

Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti +9

We evaluate the moral alignment of LLMs with human preferences in multilingual trolley problems. Building on the Moral Machine experiment, which captures over 40 million human judg…

cs.CL2024

Towards Region-aware Bias Evaluation Metrics

Angana Borah, Aparna Garimella, Rada Mihalcea

When exposed to human-generated data, language models are known to learn and amplify societal biases. While previous works introduced benchmarks that can be used to assess the bias…

cs.CL20242 cited

Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense

Siqi Shen, Lajanugen Logeswaran, Moontae Lee +3

Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense re…

cs.CL2024

Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries

Roberto Ceraolo, Dmitrii Kharlapenko, Ahmad Khan +6

Recent progress in Large Language Model (LLM) technology has changed our role in interacting with these models. Instead of primarily testing these models with questions we already…

cs.CL2024

Implicit Personalization in Language Models: A Systematic Study

Zhijing Jin, Nils Heil, Jiarui Liu +5

Implicit Personalization (IP) is a phenomenon of language models inferring a user's background from the implicit cues in the input prompts and tailoring the response based on this…