2 citations · 3 across the 5 of their papers we have counts for
5 papers
Where Did It All Go Wrong? A Hierarchical Look into Multi-Agent Error Attribution
Adi Banerjee, Anirudh Nair, Tarik Borogovac
Error attribution in Large Language Model (LLM) multi-agent systems presents a significant challenge in debugging and improving collaborative AI systems. Current approaches to pinp…
Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings
Anirudh Nair, Adi Banerjee, Laurent Mombaerts +2
Prompt engineering represents a critical bottleneck to harness the full potential of Large Language Models (LLMs) for solving complex tasks, as it requires specialized expertise, s…
VERA: Validation and Evaluation of Retrieval-Augmented Systems
Tianyu Ding, Adi Banerjee, Laurent Mombaerts +3
The increasing use of Retrieval-Augmented Generation (RAG) systems in various applications necessitates stringent protocols to ensure RAG systems accuracy, safety, and alignment wi…
Meta Knowledge for Retrieval Augmented Large Language Models
Laurent Mombaerts, Terry Ding, Adi Banerjee +3
Retrieval Augmented Generation (RAG) is a technique used to augment Large Language Models (LLMs) with contextually relevant, time-critical, or domain-specific information without a…
A Control Variate Approach for Improving Efficiency of Ensemble Monte Carlo
T. Borogovac, F. J. Alexander, P. Vakili
In this paper we present a new approach to control variates for improving computational efficiency of Ensemble Monte Carlo. We present the approach using simulation of paths of a t…