6 papers
PLOP: Cost-Based Placement of Semantic Operators in Hybrid Query Plans
Qiuyang Mang, Yufan Xiang, Hangrui Zhou +5
Recent database systems have introduced semantic operators that leverage large language models (LLMs) to filter, join, and project over structured data using natural language predi…
Playing Psychic: Using Thought Trees to Predict Reasoning Models Accuracy on Coding Tasks
Jiaxin Fang, Runyuan He, Sahil Bhatia +2
Recent advances in large language models (LLMs) have shown that test-time scaling can substantially improve model performance on complex tasks, particularly in the coding domain. U…
Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
Hanchen Li, Runyuan He, Qizheng Zhang +11
Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without parameter changes. For example, existing…
FrontierCS: Evolving Challenges for Evolving Intelligence
Qiuyang Mang, Wenhao Chai, Zhifei Li +48
We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competiti…
Automated Discovery of Test Oracles for Database Management Systems Using LLMs
Qiuyang Mang, Runyuan He, Suyang Zhong +3
Since 2020, automated testing for Database Management Systems (DBMSs) has flourished, uncovering hundreds of bugs in widely-used systems. A cornerstone of these techniques is test…
In-depth Analysis of Graph-based RAG in a Unified Framework
Yingli Zhou, Yaodong Su, Youran Sun +8
Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptab…