3 citations · 5 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards
Faisal Hamman, Chenyang Zhu, Anoop Kumar +4
RAG systems are increasingly deployed in high-stakes domains where users expect outputs to be consistent across semantically equivalent queries. However, existing systems often exh…
Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programs
Parker Glenn, Alfy Samuel, Daben Liu
Integrating LLM powered operators in declarative query languages allows for the combination of cheap and interpretable functions with powerful, generalizable language model reasoni…
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems
Chenyang Zhu, Spencer Hong, Jingyu Wu +6
The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that curre…
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sungmin Kang +9
Generative Large Language Models (LLMs)inevitably produce untruthful responses. Accurately predicting the truthfulness of these outputs is critical, especially in high-stakes setti…
FB-RAG: Improving RAG with Forward and Backward Lookup
Kushal Chawla, Alfy Samuel, Anoop Kumar +1
Traditional Retrieval-Augmented Generation (RAG) struggles with complex queries that lack strong signals to retrieve the most relevant context, forcing a trade-off between choosing…