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)…
Aligning to Social Norms and Values in Interactive Narratives
We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games—environments wherein an agent perceives and interacts with a…
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection
Warning : this paper discusses and contains content that is offensive or upsetting. The perceived toxicity of language can vary based on someone’s identity and beliefs, but this variation is often…
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of…
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Machines that can represent and describe environmental soundscapes have practical poten-tial, e.g., for audio tagging and captioning. Pre-vailing learning paradigms of audio-text connections have…
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however,…
Reframing Human-AI Collaboration for Generating Free-Text Explanations
Large language models are increasingly capa-ble of generating fluent-appearing text with relatively little task-specific supervision. But can these models accurately explain classification decisions?…
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
The common practice for training commonsense models has gone from–human–to– corpus–to–machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we…
Time Waits for No One! Analysis and Challenges of Temporal Misalignment
When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we…
Transparent Human Evaluation for Image Captioning
We establish a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machineand humangenerated captions on…