10 papers
Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track
Shivani Upadhyay, Nandan Thakur, Ronak Pradeep +3
The second edition of the TREC Retrieval Augmented Generation (RAG) Track advances research on systems that integrate retrieval and generation to address complex, real-world inform…
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…
UniRAG: Universal Retrieval Augmentation for Large Vision Language Models
Sahel Sharifymoghaddam, Shivani Upadhyay, Wenhu Chen +1
Recently, Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding (e.g., image captioning or visual question answering…