10 citations · 20 across the 9 of their papers we have counts for
9 papers
A Roadmap to Impactful Pluralistic Alignment Research
Elinor Poole-Dayan, Jillian Fisher, Atoosa Kasirzadeh +3
Pluralistic value alignment---the goal of building AI systems that represent and serve diverse human values and perspectives---has emerged as an active research agenda. Yet, there'…
Benchmarking Overton Pluralism in LLMs
Elinor Poole-Dayan, Jiayi Wu, Taylor Sorensen +2
We introduce OVERTONBENCH, a novel framework for measuring Overton pluralism in LLMs--the extent to which diverse viewpoints are represented in model outputs. We (i) formalize Over…
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…
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…
An AI-Powered Framework for Analyzing Collective Idea Evolution in Deliberative Assemblies
Elinor Poole-Dayan, Deb Roy, Jad Kabbara
In an era of increasing societal fragmentation, political polarization, and erosion of public trust in institutions, representative deliberative assemblies are emerging as a promis…
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…
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…
Are Diffusion Models Vision-And-Language Reasoners?
Benno Krojer, Elinor Poole-Dayan, Vikram Voleti +2
Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlike discriminative vision-and-language mod…
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…