2 citations · 3 across the 5 of their papers we have counts for
6 papers
Component-Aware Feedback for Self-Evolving Programs
Ethan Lin, Jinming Nian, Yi Fang
LLM-guided evolutionary search can discover complex programs, but existing methods mostly only save candidate programs and fitness scores while discarding which component edits pro…
MemRerank: Preference Memory for Personalized Product Reranking
Zhiyuan Peng, Xuyang Wu, Huaixiao Tou +2
LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffe…
Submodular Evaluation Subset Selection in Automatic Prompt Optimization
Jinming Nian, Zhiyuan Peng, Hongwei Shang +2
Automatic prompt optimization reduces manual prompt engineering, but relies on task performance measured on a small, often randomly sampled evaluation subset as its main source of…
Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers
Zhiyuan Peng, Ting-ruen Wei, Tingyu Song +1
Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often h…
ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski +1
Retrieval-augmented generation (RAG) has become integral to large language models (LLMs), particularly for conversational AI systems where user questions may reference knowledge be…
Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications
Ethan Lin, Zhiyuan Peng, Yi Fang
Recent studies have evaluated the creativity/novelty of large language models (LLMs) primarily from a semantic perspective, using benchmarks from cognitive science. However, access…