Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Many applications of text generation require incorporating different constraints to control the semantics or style of generated text. These constraints can be hard (e.g., ensuring certain keywords…
CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading
When reading a scholarly paper, scientists oftentimes wish to understand how follow-on work has built on or engages with what they are reading. While a paper itself can only discuss prior work, some…
Probing Factually Grounded Content Transfer with Factual Ablation
Despite recent success, large neural models often generate factually incorrect text. Compounding this is the lack of a standard automatic evaluation for factuality–it cannot be meaningfully improved…
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Abstractive summarization systems today produce fluent and relevant output, but often “hallucinate” statements not supported by the source text. We analyze the connection between hallucinations and…
Memory-assisted prompt editing to improve GPT-3 after deployment
Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homonym, while the…
Object Manipulation via Visual Target Localization
Object manipulation is a critical skill required for Embodied AI agents interacting with the world around them. Training agents to manipulate objects, poses many challenges. These include occlusion…
ScienceWorld: Is your Agent Smarter than a 5th Grader?
This paper presents a new benchmark, SCIENCEWORLD, to test agents’ scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science…
Staged Training for Transformer Language Models
The current standard approach to scaling transformer language models trains each model size from a different random initialization. As an alternative, we consider a staged training setup that begins…
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
While there has been a lot of research and many recent advances in neural fake news detection, defending against human-written disinformation remains underexplored. Upon analyzing current approaches…
LIMEADE: From AI Explanations to Advice Taking
Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly…