9 citations · 29 across the 11 of their papers we have counts for
11 papers
Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference
Md Mostafizer Rahman, Md Faizul Ibne Amin, Md Shahajada Mia +2
Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latency, energy consumption, and G…
CodeT5-RNN: Reinforcing Contextual Embeddings for Enhanced Code Comprehension
Md Mostafizer Rahman, Ariful Islam Shiplu, Yutaka Watanobe +3
Contextual embeddings generated by LLMs exhibit strong positional inductive biases, which can limit their ability to fully capture long-range, order-sensitive dependencies in highl…
GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generation
Jia Li, Xianjie Shi, Kechi Zhang +10
Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress for code generation. Recently, large…
LONGCODEU: Benchmarking Long-Context Language Models on Long Code Understanding
Jia Li, Xuyuan Guo, Lei Li +7
Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by…
PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing
Yuwei Zhang, Zhi Jin, Ying Xing +5
Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (L…
Large Language Model-Aware In-Context Learning for Code Generation
Jia Li, Ge Li, Chongyang Tao +3
Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. LLMs take a prompt consisting of requirement-code examples and a new requir…
STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing
Yuwei Zhang, Zhi Jin, Ying Xing +1
Bug fixing holds significant importance in software development and maintenance. Recent research has made notable progress in exploring the potential of large language models (LLMs…
Non-autoregressive Model for Full-line Code Completion
Fang Liu, Zhiyi Fu, Ge Li +3
Code completion tools are frequently used by software developers to accelerate software development by suggesting the following code elements. Completing a sequence of code tokens…
Learning to Recommend Method Names with Global Context
Fang Liu, Ge Li, Zhiyi Fu +3
In programming, the names for the program entities, especially for the methods, are the intuitive characteristic for understanding the functionality of the code. To ensure the read…
Modeling Programs Hierarchically with Stack-Augmented LSTM
Fang Liu, Lu Zhang, Zhi Jin
Programming language modeling has attracted extensive attention in recent years, and it plays an essential role in program processing fields. Statistical language models, which are…
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning
Fang Liu, Ge Li, Bolin Wei +3
Code completion, one of the most useful features in the Integrated Development Environments (IDEs), can accelerate software development by suggesting the libraries, APIs, and metho…