3 citations · 5 across the 18 of their papers we have counts for
10 papers · 2 filters
LLM Optimization Unlocks Real-Time Pairwise Reranking
Jingyu Wu, Aditya Shrivastava, Jing Zhu +3
Efficiently reranking documents retrieved from information retrieval (IR) pipelines to enhance overall quality of Retrieval-Augmented Generation (RAG) system remains an important y…
Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang +8
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in rea…
Readability Reconsidered: A Cross-Dataset Analysis of Reference-Free Metrics
Catarina G Belem, Parker Glenn, Alfy Samuel +2
Automatic readability assessment plays a key role in ensuring effective and accessible written communication. Despite significant progress, the field is hindered by inconsistent de…
A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation
Neal Gregory Lawton, Alfy Samuel, Anoop Kumar +1
A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation Download PDF Neal Gregory Lawton, Alfy Samuel, Anoop Kumar, Daben Liu Published: 20…
Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation
Xujun Peng, Anoop Kumar, Jingyu Wu +2
Retrieval-Augmented Generation (RAG) systems leverage Large Language Models (LLMs) to generate accurate and reliable responses that are grounded in retrieved context. However, LLMs…
Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation
Zhiqi Huang, Vivek Datla, Chenyang Zhu +4
We propose a method for confidence estimation in retrieval-augmented generation (RAG) systems that aligns closely with the correctness of large language model (LLM) outputs. Confid…