33 citations · 35 across the 5 of their papers we have counts for
4 papers · 1 filter
Understanding Prompt Programming Tasks and Questions
Jenny T. Liang, Chenyang Yang, Agnia Sergeyuk +2
Prompting foundation models (FMs) like large language models (LLMs) have enabled new AI-powered software features (e.g., text summarization) that previously were only possible by f…
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
Yilin Zhang, Xinran Zhao, Zora Zhiruo Wang +3
Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve actuality. However, a critical…
What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts
Chenyang Yang, Yike Shi, Qianou Ma +3
Prompt underspecification is a common challenge when interacting with LLMs. In this paper, we present an in-depth analysis of this problem, showing that while LLMs can often infer…
SPHERE: An Evaluation Card for Human-AI Systems
Qianou Ma, Dora Zhao, Xinran Zhao +6
In the era of Large Language Models (LLMs), establishing effective evaluation methods and standards for diverse human-AI interaction systems is increasingly challenging. To encoura…