activity
20182022
most citedScaling Instruction-Finetuned Language Models

1.2k citations · 1.2k across the 6 of their papers we have counts for

collaborators

14 papers

cs.CL20224 cited

Multi-Vector Retrieval as Sparse Alignment

Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu +5

Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alig…

cs.CL2022

Breaking BERT: Evaluating and Optimizing Sparsified Attention

Siddhartha Brahma, Polina Zablotskaia, David Mimno

Transformers allow attention between all pairs of tokens, but there is reason to believe that most of these connections - and their quadratic time and memory - may not be necessary…

cs.LG20221.2k cited

Scaling Instruction-Finetuned Language Models

Hyung Won Chung, Le Hou, Shayne Longpre +32

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…

cs.CL20204 cited

Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation

Ishan Jindal, Ranit Aharonov, Siddhartha Brahma +2

Deep neural models achieve some of the best results for semantic role labeling. Inspired by instance-based learning that utilizes nearest neighbors to handle low-frequency context-…

cs.CL2020

CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling

Ishan Jindal, Yunyao Li, Siddhartha Brahma +1

Semantic role labeling (SRL) identifies predicate-argument structure(s) in a given sentence. Although different languages have different argument annotations, polyglot training, th…

cs.CL2020

Small but Mighty: New Benchmarks for Split and Rephrase

Li Zhang, Huaiyu Zhu, Siddhartha Brahma +1

Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones. As a relatively new task, it is paramount to ensure the soundness of its evaluat…