24 citations · 38 across the 29 of their papers we have counts for
6 papers · 1 filter
M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation
Jiaheng Liu, Ken Deng, Congnan Liu +13
Repository-level code completion has drawn great attention in software engineering, and several benchmark datasets have been introduced. However, existing repository-level code com…
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation
Wenbo Xu, Yanan Wu, Haoran Jiang +3
Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated…
MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models
Pei Wang, Yanan Wu, Zekun Wang +12
Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benc…
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams
Ziqiang Wang, Zhixiang Chi, Yanan Wu +4
Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. Current methods predomina…
R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models
Ken Deng, Jiaheng Liu, He Zhu +13
Code completion models have made significant progress in recent years. Recently, repository-level code completion has drawn more attention in modern software development, and sever…
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Yanan Wu, Zhixiang Chi, Yang Wang +2
Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and…