activity
20212026
most citedOn the Relationship between Truth and Political Bias in Language Models

10 citations · 20 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2025

Applying Large Language Models to Characterize Public Narratives

Elinor Poole-Dayan, Daniel T Kessler, Hannah Chiou +4

Public Narratives (PNs) are key tools for leadership development and civic mobilization, yet their systematic analysis remains challenging due to their subjective interpretation an…

cs.CL2025

Computational Analysis of Conversation Dynamics through Participant Responsivity

Margaret Hughes, Brandon Roy, Elinor Poole-Dayan +2

Growing literature explores toxicity and polarization in discourse, with comparatively less work on characterizing what makes dialogue prosocial and constructive. We explore conver…

cs.CL2024★ 10 cited

On the Relationship between Truth and Political Bias in Language Models

Suyash Fulay, William Brannon, Shrestha Mohanty +4

Language model alignment research often attempts to ensure that models are not only helpful and harmless, but also truthful and unbiased. However, optimizing these objectives simul…

cs.CL2024★ 5 cited

LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users

Elinor Poole-Dayan, Deb Roy, Jad Kabbara

While state-of-the-art large language models (LLMs) have shown impressive performance on many tasks, there has been extensive research on undesirable model behavior such as halluci…

cs.CL2021★ 5 cited

An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

Nicholas Meade, Elinor Poole-Dayan, Siva Reddy

Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on. This has attracted attention to developing techniques th…