Search Results for author: Frank F. Xu

Found 25 papers, 21 papers with code

CodeRAG-Bench: Can Retrieval Augment Code Generation?

1 code implementation20 Jun 2024 Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F. Xu, Yiqing Xie, Graham Neubig, Daniel Fried

We aggregate documents from five sources for models to retrieve contexts: competition solutions, online tutorials, library documentation, StackOverflow posts, and GitHub repositories.

Code Generation RAG +1

WebArena: A Realistic Web Environment for Building Autonomous Agents

1 code implementation25 Jul 2023 Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, Graham Neubig

Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.

Hierarchical Prompting Assists Large Language Model on Web Navigation

3 code implementations23 May 2023 Abishek Sridhar, Robert Lo, Frank F. Xu, Hao Zhu, Shuyan Zhou

Large language models (LLMs) struggle on processing complicated observations in interactive decision making tasks.

Decision Making Language Modelling +1

Active Retrieval Augmented Generation

2 code implementations11 May 2023 Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig

In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.

Retrieval Sentence

Why do Nearest Neighbor Language Models Work?

1 code implementation7 Jan 2023 Frank F. Xu, Uri Alon, Graham Neubig

Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word.

Retrieval

MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages

1 code implementation16 Mar 2022 Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, Graham Neubig

While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric.

Code Generation Code Summarization

A Systematic Evaluation of Large Language Models of Code

3 code implementations26 Feb 2022 Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn

We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages.

Language Modelling

Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

2 code implementations28 Jan 2022 Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig

Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.

Language Modelling Retrieval

Capturing Structural Locality in Non-parametric Language Models

no code implementations ICLR 2022 Frank F. Xu, Junxian He, Graham Neubig, Vincent J. Hellendoorn

Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies.

Learning Structural Edits via Incremental Tree Transformations

1 code implementation ICLR 2021 Ziyu Yao, Frank F. Xu, Pengcheng Yin, Huan Sun, Graham Neubig

To show the unique benefits of modeling tree edits directly, we further propose a novel edit encoder for learning to represent edits, as well as an imitation learning method that allows the editor to be more robust.

Imitation Learning

In-IDE Code Generation from Natural Language: Promise and Challenges

no code implementations27 Jan 2021 Frank F. Xu, Bogdan Vasilescu, Graham Neubig

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs.

Code Generation Data Visualization Software Engineering

Minimally Supervised Categorization of Text with Metadata

1 code implementation1 May 2020 Yu Zhang, Yu Meng, Jiaxin Huang, Frank F. Xu, Xuan Wang, Jiawei Han

Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity.

Document Classification

How Can We Know What Language Models Know?

1 code implementation TACL 2020 Zhengbao Jiang, Frank F. Xu, Jun Araki, Graham Neubig

Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession".

StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible

1 code implementation20 Aug 2019 Anhong Guo, Junhan Kong, Michael Rivera, Frank F. Xu, Jeffrey P. Bigham

Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface.

AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging

no code implementations ACL 2019 Bill Yuchen Lin, Dong-Ho Lee, Frank F. Xu, Ouyu Lan, Xiang Ren

We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER).

Active Learning named-entity-recognition +2

Data-to-Text Generation with Style Imitation

1 code implementation Findings of the Association for Computational Linguistics 2020 Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.

Data-to-Text Generation Sentence +1

ExtRA: Extracting Prominent Review Aspects from Customer Feedback

1 code implementation EMNLP 2018 Zhiyi Luo, Shanshan Huang, Frank F. Xu, Bill Yuchen Lin, Hanyuan Shi, Kenny Zhu

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects.

Mining Cross-Cultural Differences and Similarities in Social Media

no code implementations ACL 2018 Bill Yuchen Lin, Frank F. Xu, Kenny Zhu, Seung-won Hwang

Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media.

Machine Translation Natural Language Understanding +2

Automatic Extraction of Commonsense LocatedNear Knowledge

1 code implementation ACL 2018 Frank F. Xu, Bill Yuchen Lin, Kenny Q. Zhu

LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life.

Relation Sentence

Indirect Supervision for Relation Extraction using Question-Answer Pairs

2 code implementations30 Oct 2017 Zeqiu Wu, Xiang Ren, Frank F. Xu, Ji Li, Jiawei Han

However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy.

Question Answering Relation +1

Empower Sequence Labeling with Task-Aware Neural Language Model

3 code implementations13 Sep 2017 Liyuan Liu, Jingbo Shang, Frank F. Xu, Xiang Ren, Huan Gui, Jian Peng, Jiawei Han

In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.

Language Modelling named-entity-recognition +5

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