7 papers
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…
Reranking-based Generation for Unbiased Perspective Summarization
Narutatsu Ri, Nicholas Deas, Kathleen McKeown
Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evalua…
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…
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…
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…
Rejected Dialects: Biases Against African American Language in Reward Models
Joel Mire, Zubin Trivadi Aysola, Daniel Chechelnitsky +3
Preference alignment via reward models helps build safe, helpful, and reliable large language models (LLMs). However, subjectivity in preference judgments and the lack of represent…