Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Riveter: Measuring Power and Social Dynamics Between Entities
Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and…
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Large “instruction-tuned” language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily…
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world…
Task-aware Retrieval with Instructions
We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware…
PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
Large-scale vision language (VL) models use Transformers to perform cross-modal interactions between the input text and image. These cross-modal interactions are computationally expensive and…
CREPE: Open-Domain Question Answering with False Presuppositions
When asking about unfamiliar topics, information seeking users often pose questions with false presuppositions. Most existing question answering (QA) datasets, in contrast, assume all questions have…
Nonparametric Masked Language Modeling
Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked…
HINT: Hypernetwork Instruction Tuning for Efficient Zero-Shot Generalisation
Recent NLP models have the great ability to generalise ‘zero-shot’ to new tasks using only an instruction as guidance. However, these approaches usually repeat their instructions with every input,…
Elaboration-Generating Commonsense Question Answering at Scale
In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working…
FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning
Large pre-trained models are capable of few-shot in-context learning (ICL), i.e., performing a new task by prepending a few demonstrations before the test input. However, the concatenated…