23 citations · 29 across the 8 of their papers we have counts for
4 papers · 1 filter
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
Zhuowan Li, Cheng Li, Mingyang Zhang +2
Retrieval Augmented Generation (RAG) has been a powerful tool for Large Language Models (LLMs) to efficiently process overly lengthy contexts. However, recent LLMs like Gemini-1.5…
Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke, Weize Kong, Cheng Li +3
Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performanc…
Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience
Tao Chen, Siqi Zuo, Cheng Li +3
In business and marketing, analyzing the reasons behind buying is a fundamental step towards understanding consumer behaviors, shaping business strategies, and predicting market ou…
PRewrite: Prompt Rewriting with Reinforcement Learning
Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang +2
Prompt engineering is critical for the development of LLM-based applications. However, it is usually done manually in a "trial and error" fashion that can be time consuming, ineffe…