563 citations · 813 across the 25 of their papers we have counts for
5 papers · 1 filter
Automatic Pair Construction for Contrastive Post-training
Canwen Xu, Corby Rosset, Ethan C. Chau +6
Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for…
LongCoder: A Long-Range Pre-trained Language Model for Code Completion
Daya Guo, Canwen Xu, Nan Duan +2
In this paper, we introduce a new task for code completion that focuses on handling long code input and propose a sparse Transformer model, called LongCoder, to address this task.…
RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
Tianyang Liu, Canwen Xu, Julian McAuley
Large Language Models (LLMs) have greatly advanced code auto-completion systems, with a potential for substantial productivity enhancements for developers. However, current benchma…
Small Models are Valuable Plug-ins for Large Language Models
Canwen Xu, Yichong Xu, Shuohang Wang +3
Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned wit…
Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization
Canwen Xu, Julian McAuley, Penghan Wang
We present Mirror, an open-source platform for data exploration and analysis powered by large language models. Mirror offers an intuitive natural language interface for querying da…