261 citations · 390 across the 11 of their papers we have counts for
11 papers
Self-Optimizing Multi-Agent Systems for Deep Research
Arthur Câmara, Vincent Slot, Jakub Zavrel
Given a user's complex information need, a multi-agent Deep Research system iteratively plans, retrieves, and synthesizes evidence across hundreds of documents to produce a high-qu…
Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework
Zackary Rackauckas, Arthur Câmara, Jakub Zavrel
Challenges in the automated evaluation of Retrieval-Augmented Generation (RAG) Question-Answering (QA) systems include hallucination problems in domain-specific knowledge and the l…
InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval
Hugo Abonizio, Luiz Bonifacio, Vitor Jeronymo +3
Recent work has explored Large Language Models (LLMs) to overcome the lack of training data for Information Retrieval (IR) tasks. The generalization abilities of these models have…
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio +4
Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant q…
A New Neural Search and Insights Platform for Navigating and Organizing AI Research
Marzieh Fadaee, Olga Gureenkova, Fernando Rejon Barrera +3
To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, tha…
Effective Distributed Representations for Academic Expert Search
Mark Berger, Jakub Zavrel, Paul Groth
Expert search aims to find and rank experts based on a user's query. In academia, retrieving experts is an efficient way to navigate through a large amount of academic knowledge. H…