5 papers
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
Can Jin, Hongwu Peng, Anxiang Zhang +8
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuan…
Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety
Can Jin, Rui Wu, Tong Che +10
Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignm…
GROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Kiran Thorat, Hongwu Peng, Yuebo Luo +8
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gain…
LLM-VeriPPA: Power, Performance, and Area Optimization aware Verilog Code Generation with Large Language Models
Kiran Thorat, Jiahui Zhao, Yaotian Liu +5
Large Language Models (LLMs) are gaining prominence in various fields, thanks to their ability to generate high- quality content from human instructions. This paper delves into the…
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Can Jin, Hongwu Peng, Shiyu Zhao +7
Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. Despite impressive performance, current zero-shot rel…