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…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
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…
Overview of the TREC 2023 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +5
This is the fifth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human-annotated training labels a…
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents
Nandan Thakur, Jimmy Lin, Sam Havens +3
We introduce FreshStack, a holistic framework for automatically building information retrieval (IR) evaluation benchmarks by incorporating challenging questions and answers. FreshS…
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…