researcher

Liang Ding

University of Sydney / JD Explore Academy

125 papers hereh-index 405.3k citations199 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position

sole author2
first author10
middle author97
last author8

Across the 117 of 125 papers where every author was matched, so the position is known.

fields

affiliations

University of Sydney / JD Explore Academy

Homepage

same name

Liang Ding — 9 papers, h 8
Liang Ding — 7 papers
Liang Ding — 4 papers, h 1
Liang Ding — 4 papers, h 2
Liang Ding — 4 papers, h 9
Liang Ding — 3 papers, h 23

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity

20192026

most citedCan ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

148 citations · 341 across the 98 of their papers we have counts for

collaborators

Showing cs.SEShow all

3 papers · 1 filter

cs.SE2025

A Multi-Language Object-Oriented Programming Benchmark for Large Language Models

Shuai Wang, Liang Ding, Li Shen +4

Establishing fair and robust benchmarks is essential for evaluating intelligent code generation by large language models (LLMs). Our survey of 35 existing benchmarks uncovers three…

cs.SE2025

Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models

Shengsheng Zhou, Shuai Wang, Liang Ding +5

Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicity and effectiveness. However, few-shot…

cs.SE2024

$USCD$ : Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding

Shuai Wang, Liang Ding, Li Shen +4

Large language models (LLMs) have shown remarkable capabilities in code generation. However, the effects of hallucinations (e.g., output noise) make it particularly challenging for…

A Multi-Language Object-Oriented Programming Benchmark for Large Language Models

Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models

USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding

$USCD$ : Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding