3 citations · 4 across the 5 of their papers we have counts for
5 papers
Contextual Biasing for LLM-Based ASR with Hotword Retrieval and Reinforcement Learning
YuXiang Kong, JunFeng Hou, Jian Tang +3
Large language model (LLM)-based automatic speech recognition (ASR) has recently achieved strong performance across diverse tasks, yet contextual biasing for named entities and hot…
Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey
Shengyue Guan, Jindong Wang, Jiang Bian +3
This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings. Using a PRISMA-inspired framework, we systematically revi…
AMPO: Automatic Multi-Branched Prompt Optimization
Sheng Yang, Yurong Wu, Yan Gao +10
Prompt engineering is very important to enhance the performance of large language models (LLMs). When dealing with complex issues, prompt engineers tend to distill multiple pattern…
StraGo: Harnessing Strategic Guidance for Prompt Optimization
Yurong Wu, Yan Gao, Bin Benjamin Zhu +6
Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt…
AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding
Safwat Ali Khan, Wenyu Wang, Yiran Ren +5
Nearly a decade of research in software engineering has focused on automating mobile app testing to help engineers in overcoming the unique challenges associated with the software…