Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL)…
Editing Models with Task Arithmetic
Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine…
InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions
In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the…
Selective Annotation Makes Language Models Better Few-Shot Learners
Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they learn a new task…
Binding Language Models in Symbolic Languages
Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free…
Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics
A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the"move ahead"action will always move the agent forward by a fixed…
Can AI language models replace human participants?
Recent work suggests that language models such as GPT can make human-like judgments across a number of domains. We explore whether and when language models might replace human participants in…
S2abEL: A Dataset for Entity Linking from Scientific Tables
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When applied to tables in…
Old dog, new trick: Reservoir computing advances machine learning for climate modeling
Physics-informed machine learning (ML) applied to geophysical simulation is developing explosively. Recently, graph neural net and vision transformer architectures have shown 1-7 day global weather…
Answering Questions by Meta-Reasoning over Multiple Chains of Thought
Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple…