Search Results for author: Xi Victoria Lin

Found 28 papers, 19 papers with code

ColloQL: Robust Text-to-SQL Over Search Queries

1 code implementation EMNLP (intexsempar) 2020 Karthik Radhakrishnan, Arvind Srikantan, Xi Victoria Lin

Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of non-tech-savvy end users.

Data Augmentation Text-To-SQL

RA-DIT: Retrieval-Augmented Dual Instruction Tuning

no code implementations2 Oct 2023 Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, Luke Zettlemoyer, Scott Yih

Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build.

Reimagining Retrieval Augmented Language Models for Answering Queries

no code implementations1 Jun 2023 Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Scott Yih

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison.

Question Answering Retrieval

Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model

no code implementations23 May 2023 Leo Z. Liu, Tim Dettmers, Xi Victoria Lin, Veselin Stoyanov, Xian Li

Large and sparse feed-forward networks (S-FFN) such as Mixture-of-Experts (MoE) have demonstrated to be an efficient approach for scaling up Transformers model size for pretraining large language models.

Avg Language Modelling +1

LEVER: Learning to Verify Language-to-Code Generation with Execution

1 code implementation16 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.

Arithmetic Reasoning Code Generation +2

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

no code implementations22 Dec 2022 Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O'Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, Ves Stoyanov

To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.

Language Modelling Meta-Learning +2

Lifting the Curse of Multilinguality by Pre-training Modular Transformers

no code implementations NAACL 2022 Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe

Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages.

named-entity-recognition Named Entity Recognition +3

On Continual Model Refinement in Out-of-Distribution Data Streams

no code implementations ACL 2022 Bill Yuchen Lin, Sida Wang, Xi Victoria Lin, Robin Jia, Lin Xiao, Xiang Ren, Wen-tau Yih

Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting.

Benchmarking Continual Learning

Efficient Large Scale Language Modeling with Mixtures of Experts

no code implementations20 Dec 2021 Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov

This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.

Language Modelling

Learning to Synthesize Data for Semantic Parsing

1 code implementation NAACL 2021 Bailin Wang, Wenpeng Yin, Xi Victoria Lin, Caiming Xiong

Moreover, explicitly modeling compositions using PCFG leads to a better exploration of unseen programs, thus generate more diverse data.

Domain Generalization Semantic Parsing +3

FeTaQA: Free-form Table Question Answering

1 code implementation1 Apr 2021 Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Nick Schoelkopf, Riley Kong, Xiangru Tang, Murori Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev

Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.

Question Answering Retrieval +2

NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands

no code implementations3 Mar 2021 Mayank Agarwal, Tathagata Chakraborti, Quchen Fu, David Gros, Xi Victoria Lin, Jaron Maene, Kartik Talamadupula, Zhongwei Teng, Jules White

The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

2 code implementations Findings of the Association for Computational Linguistics 2020 Xi Victoria Lin, Richard Socher, Caiming Xiong

We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing.

Deep Attention Semantic Parsing +1

ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries

1 code implementation19 Oct 2020 Karthik Radhakrishnan, Arvind Srikantan, Xi Victoria Lin

Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of non-tech-savvy end users.

Data Augmentation Text-To-SQL

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

1 code implementation ICLR 2021 Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, Caiming Xiong

We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data.

Inductive Bias Language Modelling +3

Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation

1 code implementation ACL 2020 Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani, Bryan McCann, Vicente Ordonez, Caiming Xiong

Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models.

Word Embeddings

SParC: Cross-Domain Semantic Parsing in Context

5 code implementations ACL 2019 Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev

The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.

Semantic Parsing Text-To-SQL

Cannot find the paper you are looking for? You can Submit a new open access paper.