rag-evaluation

LLM-FACETS is an open-source evaluation framework designed to help practitioners assess LLM transparency and accountability with a focus on privacy and data flow transparency. It provides a browser interface, plugin architecture, and supports multiple auditing mechanisms including token-level log-probability visualization and RAG Triad metrics.

This paper presents a comparative evaluation of embedding models and generator backends for Khmer-language retrieval-augmented question answering in the telecom domain, finding that BGE-M3 performs best for retrieval while generator strengths vary across metrics.

RARE introduces a redundancy-aware retrieval evaluation framework that decomposes documents into atomic facts to create realistic benchmarks for high-similarity corpora like finance, legal, and patents, revealing significant performance drops in existing retrievers.