ndcg-16d136c0·3 events·first seen Aliases: NDCG, NDCG@3
A new arXiv preprint proposes using model merging (Model Soup and Slerp) as a training-free alternative to fine-tuning for conversational information retrieval, avoiding catastrophic forgetting of ad-hoc retrieval capabilities. Experiments on standard ad-hoc and conversational retrieval benchmarks show up to 15% higher NDCG@3 under zero-shot conditions. The approach enables a single retrieval model to operate across both ad-hoc and conversational settings without retraining.
A new arXiv preprint introduces a Bradley-Terry (BT) model-based methodology for ranking recommendation algorithms in a way that accounts for dataset characteristics such as sparsity, sequential structure, and scale. The authors argue that naive metric aggregation (e.g., averaging NDCG) produces misleading rankings and propose BT trees and covariate-extended BT models as alternatives. The framework also enables ranking predictions on unseen datasets without running the models, and includes a new metric for ranking consistency.
Mistral AI has launched Search Toolkit in public preview, an open-source framework that unifies document ingestion, retrieval, and evaluation into a single composable pipeline for AI applications. The toolkit ships with BM25 sparse retrieval, dense embedding-based retrieval, hybrid configurations, and built-in metrics (recall, precision, MRR, NDCG), targeting enterprise RAG workflows, domain-specific retrieval, and agentic systems. It integrates with MCP-based Connectors for live data access from CRMs, code repositories, and productivity tools. CMA CGM is cited as a production user, combining Search Toolkit with Voxtral for real-time fake news detection across audio sources.