9 citations · 23 across the 6 of their papers we have counts for
7 papers
On the Comprehensibility of Multi-structured Financial Documents using LLMs and Pre-processing Tools
Shivani Upadhyay, Messiah Ataey, Syed Shariyar Murtaza +2
The proliferation of complex structured data in hybrid sources, such as PDF documents and web pages, presents unique challenges for current Large Language Models (LLMs) and Multi-m…
Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses
Sahel Sharifymoghaddam, Shivani Upadhyay, Nandan Thakur +2
Battles, or side-by-side comparisons in so-called arenas that elicit human preferences, have emerged as a popular approach for assessing the output quality of LLMs. Recently, this…
Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges
Nandan Thakur, Ronak Pradeep, Shivani Upadhyay +3
Retrieval-augmented generation (RAG) enables large language models (LLMs) to generate answers with citations from source documents containing "ground truth", thereby reducing syste…
The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models
Ronak Pradeep, Nandan Thakur, Shivani Upadhyay +3
Large Language Models (LLMs) have significantly enhanced the capabilities of information access systems, especially with retrieval-augmented generation (RAG). Nevertheless, the eva…
Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework
Ronak Pradeep, Nandan Thakur, Shivani Upadhyay +3
This report provides an initial look at partial results from the TREC 2024 Retrieval-Augmented Generation (RAG) Track. We have identified RAG evaluation as a barrier to continued p…
A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Look
Shivani Upadhyay, Ronak Pradeep, Nandan Thakur +5
The application of large language models to provide relevance assessments presents exciting opportunities to advance information retrieval, natural language processing, and beyond,…