6 citations · 14 across the 20 of their papers we have counts for
9 papers · 1 filter
Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment
Yunfan Zhang, Kathleen McKeown, Smaranda Muresan
Large Language Models (LLMs) are typically trained to reflect a relatively uniform set of values, which limits their applicability to tasks that require understanding of nuanced hu…
Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond
Zachary Horvitz, Raghav Singhal, Hao Zou +4
The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving. While current tooling for reasoning…
iBERT: Interpretable Embeddings via Sense Decomposition
Vishal Anand, Milad Alshomary, Kathleen McKeown
We present iBERT (interpretable-BERT), an encoder to produce inherently interpretable and controllable embeddings - designed to modularize and expose the discriminative cues presen…
ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges
Cheng Qian, Hongyi Du, Hongru Wang +6
Recent progress in large language models (LLMs) has enabled substantial advances in solving mathematical problems. However, existing benchmarks often fail to reflect the complexity…
DYSTIL: Dynamic Strategy Induction with Large Language Models for Reinforcement Learning
Borui Wang, Kathleen McKeown, Rex Ying
Reinforcement learning from expert demonstrations has long remained a challenging research problem, and existing state-of-the-art methods using behavioral cloning plus further RL t…
Forecasting Conversation Derailments Through Generation
Yunfan Zhang, Kathleen McKeown, Smaranda Muresan
Forecasting conversation derailment can be useful in real-world settings such as online content moderation, conflict resolution, and business negotiations. However, despite languag…
Layered Insights: Generalizable Analysis of Authorial Style by Leveraging All Transformer Layers
Milad Alshomary, Nikhil Reddy Varimalla, Vishal Anand +2
We propose a new approach for the authorship attribution task that leverages the various linguistic representations learned at different layers of pre-trained transformer-based mod…
The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination
Yuji Zhang, Sha Li, Cheng Qian +8
Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which err…
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Raghav Singhal, Zachary Horvitz, Ryan Teehan +4
Diffusion models produce impressive results in modalities ranging from images and video to protein design and text. However, generating samples with user-specified properties remai…