2 citations · 4 across the 13 of their papers we have counts for
16 papers
ToMStories: Interpreting Theory-of-Mind in Synthetic Narratives
Melanie Subbiah, Chia-En Jacklyn Tsai, Kathleen McKeown
Understanding people often requires inferring what they feel or want from how they behave rather than from what they state outright. We ask whether language models can make these i…
Empath: Tracing Multi-Level Emotion Dynamics in Crisis Counseling Dialogues
Ziwei Gong, Yuchen Huang, Wen Liang +4
Emotion dynamics are critical for understanding crisis-support conversations, yet most computational work treats emotion as static utterance-level labels. We introduce EMPATH, a fr…
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…
Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts
Hao Zou, Zachary Horvitz, Chandhru Karthick +2
Summaries of real-world events can become outdated as contexts evolve and new information arrives. A common response is to generate a new summary from the updated context, but full…
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…
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…
Guiding LLM Decision-Making with Fairness Reward Models
Zara Hall, Melanie Subbiah, Thomas P Zollo +2
Large language models are increasingly used to support high-stakes decisions, potentially influencing who is granted bail or receives a loan. Naive chain-of-thought sampling can im…
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…
Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding
Melanie Subbiah, Akankshya Mishra, Grace Kim +3
Determining faithfulness of a claim to a source document is an important problem across many domains. This task is generally treated as a binary judgment of whether the claim is su…
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…
STORYSUMM: Evaluating Faithfulness in Story Summarization
Melanie Subbiah, Faisal Ladhak, Akankshya Mishra +3
Human evaluation has been the gold standard for checking faithfulness in abstractive summarization. However, with a challenging source domain like narrative, multiple annotators ca…
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
Liyan Tang, Igor Shalyminov, Amy Wing-mei Wong +11
Single document news summarization has seen substantial progress on faithfulness in recent years, driven by research on the evaluation of factual consistency, or hallucinations. We…
Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers
Melanie Subbiah, Sean Zhang, Lydia B. Chilton +1
We evaluate recent Large Language Models (LLMs) on the challenging task of summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled timelines. Impo…