5 code implementations • NeurIPS 2023 • Chunting Zhou, PengFei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, Omer Levy
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences.
1 code implementation • 16 Feb 2023 • Ansong Ni, Srini Iyer, Dragomir Radev, Ves Stoyanov, Wen-tau Yih, Sida I. Wang, Xi Victoria Lin
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation.
Ranked #2 on Semantic Parsing on spider
no code implementations • 8 Dec 2022 • Hila Gonen, Srini Iyer, Terra Blevins, Noah A. Smith, Luke Zettlemoyer
Language models can be prompted to perform a wide variety of zero- and few-shot learning problems.
no code implementations • 25 May 2022 • Badr AlKhamissi, Faisal Ladhak, Srini Iyer, Ves Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
Cultural Vocal Bursts Intensity Prediction Few-Shot Learning +1
no code implementations • NAACL 2022 • Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, Zornitsa Kozareva
Self-supervised pretraining has made few-shot learning possible for many NLP tasks.
1 code implementation • NAACL 2022 • Alexander R. Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, Mona Diab
One goal of answer summarization is to produce a summary that reflects the range of answer perspectives.
no code implementations • 17 Apr 2021 • Alexander R. Fabbri, Xiaojian Wu, Srini Iyer, Mona Diab
A major obstacle for multi-perspective, abstractive answer summarization is the absence of a dataset to provide supervision for producing such summaries.
1 code implementation • ICLR 2021 • Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Ranked #14 on Question Answering on HotpotQA