Search Results for author: Yi Mao

Found 23 papers, 9 papers with code

RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation

1 code implementation22 Mar 2023 Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen

The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository.

Code Completion Language Modelling +1

Exploring Distributional Shifts in Large Language Models for Code Analysis

no code implementations16 Mar 2023 Shushan Arakelyan, Rocktim Jyoti Das, Yi Mao, Xiang Ren

We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data.

Code Generation Code Summarization

Generation-Augmented Query Expansion For Code Retrieval

no code implementations20 Dec 2022 Dong Li, Yelong Shen, Ruoming Jin, Yi Mao, Kuan Wang, Weizhu Chen

Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet.

Code Generation Retrieval

HyperTuning: Toward Adapting Large Language Models without Back-propagation

no code implementations22 Nov 2022 Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen

Fine-tuning large language models for different tasks can be costly and inefficient, and even methods that reduce the number of tuned parameters still require full gradient-based optimization.

Language Modelling

Explanations from Large Language Models Make Small Reasoners Better

no code implementations13 Oct 2022 Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, Wenhu Chen, Xifeng Yan

Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.

Explanation Generation In-Context Learning +1

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation

no code implementations NAACL 2022 Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, Jianfeng Gao

To address these challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.

Dialogue Generation Language Modelling

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

2 code implementations ACL 2022 Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, Bill Dolan

Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.

Hallucination Sentence +1

Finetuning Pretrained Transformers into RNNs

1 code implementation EMNLP 2021 Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith

Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.

Language Modelling Machine Translation +1

Linear Polarization of the 21 cm Line from the Epoch of Reionization

no code implementations27 Jan 2021 Bohua Li, Jianrong Tan, Yi Mao

The 21 cm linear polarization due to Thomson scattering off free electrons can probe the distribution of neutral hydrogen in the intergalactic medium during the epoch of reionization, complementary to the 21 cm temperature fluctuations.

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

The impact of inhomogeneous subgrid clumping on cosmic reionization II: modelling stochasticity

1 code implementation5 Jan 2021 Michele Bianco, Ilian T. Iliev, Kyungjin Ahn, Sambit K. Giri, Yi Mao, Hyunbae Park, Paul R. Shapiro

Unresolved fluctuations in numerical simulations and analytical calculations are included using a gas clumping factor, typically assumed to be independent of the local environment.

Cosmology and Nongalactic Astrophysics

Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning

no code implementations EMNLP 2020 Tao Shen, Yi Mao, Pengcheng He, Guodong Long, Adam Trischler, Weizhu Chen

In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text.

Entity Linking Knowledge Base Completion +5

Estimation of HII Bubble Size Distribution from 21cm Power Spectrum with Artificial Neural Networks

no code implementations19 Feb 2020 Hayato Shimabukuro, Yi Mao, Jianrong Tan

The bubble size distribution of ionized hydrogen regions probes the information about the morphology of \HII\ bubbles during the reionization.

Cosmology and Nongalactic Astrophysics

Conditional Self-Attention for Query-based Summarization

no code implementations18 Feb 2020 Yujia Xie, Tianyi Zhou, Yi Mao, Weizhu Chen

Thereby, the contextual dependencies modeled by CSA will be highly relevant to the query.

X-SQL: reinforce schema representation with context

no code implementations21 Aug 2019 Pengcheng He, Yi Mao, Kaushik Chakrabarti, Weizhu Chen

In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query.

IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

no code implementations13 Sep 2018 Tianze Shi, Kedar Tatwawadi, Kaushik Chakrabarti, Yi Mao, Oleksandr Polozov, Weizhu Chen

We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.

Action Parsing Text-To-SQL

Robust Text-to-SQL Generation with Execution-Guided Decoding

1 code implementation9 Jul 2018 Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, Rishabh Singh

We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.

Semantic Parsing Text-To-SQL

Action-depedent Control Variates for Policy Optimization via Stein's Identity

2 code implementations30 Oct 2017 Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems.

Policy Gradient Methods reinforcement-learning +1

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