no code implementations • 11 Apr 2024 • Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, Tao Yu
Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity.
no code implementations • 12 Feb 2024 • Victor Zhong, Dipendra Misra, Xingdi Yuan, Marc-Alexandre Côté
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following.
1 code implementation • 20 Sep 2023 • Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, Tao Yu
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback.
1 code implementation • 20 Dec 2022 • Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge.
1 code implementation • 25 Oct 2022 • Victor Zhong, Weijia Shi, Wen-tau Yih, Luke Zettlemoyer
Moreover, existing models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions.
1 code implementation • 13 Oct 2022 • Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs).
1 code implementation • 30 Sep 2022 • Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel
Recent work has shown that augmenting environments with language descriptions improves policy learning.
1 code implementation • 17 Feb 2022 • Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah Goodman, Tim Rocktäschel, Edward Grefenstette
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.
1 code implementation • 16 Jan 2022 • Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on Task-Oriented Dialogue Systems on KVRET
no code implementations • NeurIPS 2021 • Victor Zhong, Austin Hanjie, Sida Wang, Karthik Narasimhan, Luke Zettlemoyer
We hope SILG enables the community to quickly identify new methodolo- gies for language grounding that generalize to a diverse set of environments and their associated challenges.
1 code implementation • 20 Oct 2021 • Victor Zhong, Austin W. Hanjie, Sida I. Wang, Karthik Narasimhan, Luke Zettlemoyer
We hope SILG enables the community to quickly identify new methodologies for language grounding that generalize to a diverse set of environments and their associated challenges.
1 code implementation • Findings (ACL) 2021 • Machel Reid, Victor Zhong
Moreover, compared to previous methods on unsupervised data synthesis, our method results in higher quality parallel style pairs and improves model performance.
1 code implementation • 19 Jan 2021 • Austin W. Hanjie, Victor Zhong, Karthik Narasimhan
We investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals describing the environment dynamics.
1 code implementation • EMNLP 2020 • Victor Zhong, Mike Lewis, Sida I. Wang, Luke Zettlemoyer
We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e. g. new database schemas).
Ranked #6 on Text-To-SQL on SParC
no code implementations • ICLR 2020 • Victor Zhong, Tim Rocktäschel, Edward Grefenstette
In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.
1 code implementation • 18 Oct 2019 • Victor Zhong, Tim Rocktäschel, Edward Grefenstette
In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.
1 code implementation • ACL 2019 • Victor Zhong, Luke Zettlemoyer
Conversational machine reading systems help users answer high-level questions (e. g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made(e. g. whether they need certain income levels or veteran status).
2 code implementations • ACL 2019 • Sewon Min, Victor Zhong, Luke Zettlemoyer, Hannaneh Hajishirzi
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs.
Ranked #65 on Question Answering on HotpotQA
no code implementations • ICLR 2019 • Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, Richard Socher
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document.
Ranked #5 on Question Answering on WikiHop
no code implementations • ACL 2018 • Victor Zhong, Caiming Xiong, Richard Socher
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Automatic Speech Recognition (ASR) Dialogue State Tracking +3
1 code implementation • ACL 2018 • Sewon Min, Victor Zhong, Richard Socher, Caiming Xiong
Neural models for question answering (QA) over documents have achieved significant performance improvements.
Ranked #3 on Question Answering on NewsQA
2 code implementations • 19 May 2018 • Victor Zhong, Caiming Xiong, Richard Socher
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
Dialogue State Tracking Multi-domain Dialogue State Tracking +1
1 code implementation • ICLR 2018 • Caiming Xiong, Victor Zhong, Richard Socher
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate.
Ranked #28 on Question Answering on SQuAD1.1 dev
2 code implementations • EMNLP 2017 • Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, Christopher D. Manning
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
Ranked #7 on Relation Extraction on Re-TACRED
15 code implementations • ICLR 2018 • Victor Zhong, Caiming Xiong, Richard Socher
A significant amount of the world's knowledge is stored in relational databases.
Ranked #9 on Code Generation on WikiSQL
6 code implementations • 5 Nov 2016 • Caiming Xiong, Victor Zhong, Richard Socher
Several deep learning models have been proposed for question answering.
Ranked #2 on Open-Domain Question Answering on SQuAD1.1
11 code implementations • 24 Jun 2015 • Ankit Kumar, Ozan .Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #66 on Sentiment Analysis on SST-2 Binary classification