6 citations · 14 across the 33 of their papers we have counts for
7 papers · 1 filter
Building A Coding Assistant via the Retrieval-Augmented Language Model
Xinze Li, Hanbin Wang, Zhenghao Liu +6
Pretrained language models have shown strong effectiveness in code-related tasks, such as code retrieval, code generation, code summarization, and code completion tasks. In this pa…
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
Xinze Li, Sen Mei, Zhenghao Liu +9
Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To a…
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
Weiqing Yang, Hanbin Wang, Zhenghao Liu +7
Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human d…
Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression
Xinze Li, Zhenghao Liu, Chenyan Xiong +4
Large language models (LLMs) require lengthy prompts as the input context to produce output aligned with user intentions, a process that incurs extra costs during inference. In thi…
Cleaner Pretraining Corpus Curation with Neural Web Scraping
Zhipeng Xu, Zhenghao Liu, Yukun Yan +3
The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation,…
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling
Zhipeng Xu, Zhenghao Liu, Yukun Yan +7
Large Language Models (LLMs) have demonstrated strong performance across a wide range of NLP tasks. However, they often exhibit suboptimal behaviors and inconsistencies when expose…
LegalDuet: Learning Fine-grained Representations for Legal Judgment Prediction via a Dual-View Contrastive Learning
Buqiang Xu, Xin Dai, Zhenghao Liu +7
Legal Judgment Prediction (LJP) is a fundamental task of legal artificial intelligence, aiming to automatically predict the judgment outcomes of legal cases. Existing LJP models pr…