3k citations · 3k across the 15 of their papers we have counts for
19 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…
Ownership in AI-Assisted Everyday Tasks
Megan Wei, Melanie Subbiah, Audrey Lee +4
When does work done with AI still feel like ours? As AI becomes woven into everyday tasks, we must examine what happens to our sense of ownership and contribution when a machine sh…
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…
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…
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…
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…
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…
Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
Gengyu Wang, Kate Harwood, Lawrence Chillrud +3
We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fac…
Unsupervised Selective Rationalization with Noise Injection
Adam Storek, Melanie Subbiah, Kathleen McKeown
A major issue with using deep learning models in sensitive applications is that they provide no explanation for their output. To address this problem, unsupervised selective ration…
Towards Detecting Harmful Agendas in News Articles
Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua +3
Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection…
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Sharon Levy, Emily Allaway, Melanie Subbiah +4
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe. One such typ…
Mitigating Covertly Unsafe Text within Natural Language Systems
Alex Mei, Anisha Kabir, Sharon Levy +7
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threa…
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typicall…