9 papers
Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
Melanie Subbiah, Haaris Mian, Nicholas Deas +3
Increasingly, studies are exploring using Large Language Models (LLMs) for accelerated or scaled qualitative analysis of text data. While we can compare LLM accuracy against human…
Counterfactual Simulatability of LLM Explanations for Generation Tasks
Marvin Limpijankit, Yanda Chen, Melanie Subbiah +2
LLMs can be unpredictable, as even slight alterations to the prompt can cause the output to change in unexpected ways. Thus, the ability of models to accurately explain their behav…
Artificial Impressions: Evaluating Large Language Model Behavior Through the Lens of Trait Impressions
Nicholas Deas, Kathleen McKeown
We introduce and study artificial impressions--patterns in LLMs' internal representations of prompts that resemble human impressions and stereotypes based on language. We fit linea…
AdvSumm: Adversarial Training for Bias Mitigation in Text Summarization
Mukur Gupta, Nikhil Reddy Varimalla, Nicholas Deas +2
Large Language Models (LLMs) have achieved impressive performance in text summarization and are increasingly deployed in real-world applications. However, these systems often inher…
Data Caricatures: On the Representation of African American Language in Pretraining Corpora
Nicholas Deas, Blake Vente, Amith Ananthram +5
With a combination of quantitative experiments, human judgments, and qualitative analyses, we evaluate the quantity and quality of African American Language (AAL) representation in…
Summarization of Opinionated Political Documents with Varied Perspectives
Nicholas Deas, Kathleen McKeown
Global partisan hostility and polarization has increased, and this polarization is heightened around presidential elections. Models capable of generating accurate summaries of dive…