9 papers
On the Limits of Steering Vectors for Preference-Aligned Generation
Melanie Subbiah, Zara Hall, Kathleen McKeown
Steering vectors have emerged as a promising approach to controlled text generation, offering interpretable, training-free mechanisms for shaping model outputs. However, their prac…
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…
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…
Computational Representations of Character Significance in Novels
Haaris Mian, Melanie Subbiah, Sharon Marcus +2
Characters in novels have typically been modeled based on their presence in scenes in narrative, considering aspects like their actions, named mentions, and dialogue. This concepti…
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…
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…