activity
20222024
most citedMulti-lingual Evaluation of Code Generation Models

29 citations · 33 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

Learning Code Preference via Synthetic Evolution

Jiawei Liu, Thanh Nguyen, Mingyue Shang +5

Large Language Models (LLMs) have recently demonstrated remarkable coding capabilities. However, assessing code generation based on well-formed properties and aligning it with deve…

cs.LG2023★ 1 cited

Exploring Continual Learning for Code Generation Models

Prateek Yadav, Qing Sun, Hantian Ding +9

Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance. However, libraries are upgraded or deprecated very frequently and re-training larg…

cs.CL2023

A Static Evaluation of Code Completion by Large Language Models

Hantian Ding, Varun Kumar, Yuchen Tian +9

Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate…

cs.LG2023★ 1 cited

Greener yet Powerful: Taming Large Code Generation Models with Quantization

Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad +13

ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently,…

cs.CV2023★ 2 cited

Automatic detection of aerial survey ground control points based on Yolov5-OBB

Cheng Chuanxiang, Yang Jia, Wang Chao +5

The use of ground control points (GCPs) for georeferencing is the most common strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time their collection repres…

cs.LG2022★ 29 cited

Multi-lingual Evaluation of Code Generation Models

Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang +22

We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are generated…

cs.CL2022

ContraCLM: Contrastive Learning For Causal Language Model

Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad +9

Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we prese…