19 citations · 45 across the 11 of their papers we have counts for
11 papers
ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?
Siddhant Waghjale, Vishruth Veerendranath, Zora Zhiruo Wang +1
Although large language models (LLMs) have been largely successful in generating functionally correct programs, conditioning models to produce efficient solutions while ensuring co…
What Are Tools Anyway? A Survey from the Language Model Perspective
Zhiruo Wang, Zhoujun Cheng, Hao Zhu +2
Language models (LMs) are powerful yet mostly for text generation tasks. Tools have substantially enhanced their performance for tasks that require complex skills. However, many wo…
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks
Zhiruo Wang, Daniel Fried, Graham Neubig
Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs. However, using primitive functions often leads to verbose and error-pr…
Learning to Filter Context for Retrieval-Augmented Generation
Zhiruo Wang, Jun Araki, Zhengbao Jiang +2
On-the-fly retrieval of relevant knowledge has proven an essential element of reliable systems for tasks such as open-domain question answering and fact verification. However, beca…
API-Assisted Code Generation for Question Answering on Varied Table Structures
Yihan Cao, Shuyi Chen, Ryan Liu +2
A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific log…
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
I-Chun Chern, Zhiruo Wang, Sanjan Das +3
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastiv…
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Zhengbao Jiang, Luyu Gao, Jun Araki +4
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and d…
Execution-Based Evaluation for Open-Domain Code Generation
Zhiruo Wang, Shuyan Zhou, Daniel Fried +1
To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset. OD…
DocPrompting: Generating Code by Retrieving the Docs
Shuyan Zhou, Uri Alon, Frank F. Xu +3
Publicly available source-code libraries are continuously growing and changing. This makes it impossible for models of code to keep current with all available APIs by simply traini…
Table Retrieval May Not Necessitate Table-specific Model Design
Zhiruo Wang, Zhengbao Jiang, Eric Nyberg +1
Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent wo…
MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages
Zhiruo Wang, Grace Cuenca, Shuyan Zhou +2
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 application…