306 citations · 386 across the 87 of their papers we have counts for
13 papers · 1 filter
Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing
Xueying Du, Mingwei Liu, Hanlin Wang +3
Software crash bugs cause unexpected program behaviors or even abrupt termination, thus demanding immediate resolution. However, resolving crash bugs can be challenging due to thei…
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
Zecheng Tang, Kaifeng Qi, Juntao Li +1
Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial e…
G-SPEED: General SParse Efficient Editing MoDel
Haoke Zhang, Yue Wang, Juntao Li +2
Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. Through human-model interactions, LLMs can automati…
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Yue Wang, Xinrui Wang, Juntao Li +5
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks. The success of instruction tuning dep…
GameEval: Evaluating LLMs on Conversational Games
Dan Qiao, Chenfei Wu, Yaobo Liang +2
The rapid advancements in large language models (LLMs) have presented challenges in evaluating those models. Existing evaluation methods are either reference-based or preference ba…
CMD: a framework for Context-aware Model self-Detoxification
Zecheng Tang, Keyan Zhou, Juntao Li +5
Text detoxification aims to minimize the risk of language models producing toxic content. Existing detoxification methods of directly constraining the model output or further train…
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!
Zecheng Tang, Pinzheng Wang, Keyan Zhou +3
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space. However, there exist nonnegligible gaps between tra…
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Tong Wu, Zhihao Fan, Xiao Liu +9
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generatio…
Test-Time Adaptation with Perturbation Consistency Learning
Yi Su, Yixin Ji, Juntao Li +2
Currently, pre-trained language models (PLMs) do not cope well with the distribution shift problem, resulting in models trained on the training set failing in real test scenarios.…
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing
Ziming Huang, Zhuoxuan Jiang, Ke Wang +3
Currently, human-bot symbiosis dialog systems, e.g., pre- and after-sales in E-commerce, are ubiquitous, and the dialog routing component is essential to improve the overall effici…
RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformer
Pei Guo, Yisheng Xiao, Juntao Li +1
Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance. However, existing NAT mod…
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Yisheng Xiao, Ruiyang Xu, Lijun Wu +4
Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization,…
Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study
Hai Ye, Yuyang Ding, Juntao Li +1
A deployed question answering (QA) model can easily fail when the test data has a distribution shift compared to the training data. Robustness tuning (RT) methods have been widely…