9 citations · 20 across the 10 of their papers we have counts for
10 papers
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…
W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
Jinming Nian, Zhiyuan Peng, Qifan Wang +1
In knowledge-intensive tasks such as open-domain question answering (OpenQA), large language models (LLMs) often struggle to generate factual answers, relying solely on their inter…
Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models
Xuyang Wu, Zhiyuan Peng, Krishna Sravanthi Rajanala Sai +2
Effective passage retrieval and reranking methods have been widely utilized to identify suitable candidates in open-domain question answering tasks, recent studies have resorted to…
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
Zhiyuan Peng, Xuyang Wu, Qifan Wang +2
Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the…
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
Zhiyuan Peng, Xuyang Wu, Qifan Wang +1
Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the challenges in DR is…
DIANES: A DEI Audit Toolkit for News Sources
Xiaoxiao Shang, Zhiyuan Peng, Qiming Yuan +4
Professional news media organizations have always touted the importance that they give to multiple perspectives. However, in practice the traditional approach to all-sides has favo…