Beyond Benchmarks: Evaluating Embedding Model Similarity for RAG Systems (arXiv, July 2024)

Overview

This entry indexes the arXiv paper "Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systems" (arXiv:2407.08275, July 2024).

| Field | Detail | |---|---| | Authors | Laura Caspari, Kanishka Ghosh Dastidar, Saber Zerhoudi, Jelena Mitrovic, Michael Granitzer | | Published | July 2024 (arXiv) | | Link | https://arxiv.org/abs/2407.08275 | | Category | Embedding models |

Key points

• The paper argues that conventional leaderboard-style benchmark results are not sufficient for selecting embedding models in Retrieval-Augmented Generation (RAG) systems.
• It instead proposes evaluating the similarity between embedding models, examining when different models exhibit comparable retrieval behavior — useful for model substitution, ensembling, and redundancy analysis in RAG pipelines.
• The work sits at the intersection of embedding research and large-scale search/retrieval, where questions of efficiency, scalability, and user-intent understanding remain central.

Context from the forum discussion

The post situates the paper within the LLM-era retrieval stack:

1. Input & representation: queries, documents, and context encoded as dense or sparse vectors or structured prompts. 2. Core modules: retriever, re-ranker, planner, memory, and tool interfaces composed sequentially or in parallel. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, RLHF-style process rewards, and synthetic data. 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.

Typical evaluation setups referenced include datasets such as MS MARCO, BEIR, and Natural Questions, with metrics like nDCG@10, MRR, Recall@k, Hit@k, plus latency and token-cost measurements.

Practical engineering takeaways

• Latency: use cascaded retrieval with early stopping, cache hot queries, and apply asynchronous re-ranking.
• Data: manage PII, index versioning, and roll-back-safe embedding versions.
• Quality: verify that offline metric gains translate into online satisfaction; cross-validate LLM-as-judge scores with human review.
• Safety: guard open retrieval against poisoning and bias via source allowlists and output filtering.
• Cost: route to smaller models, use distillation, and combine sparse + dense retrieval.

Related entries

• The Scandinavian Embedding Benchmarks — multilingual embedding assessment
• Arctic-Embed 2.0: Multilingual Retrieval Without Compromise (arXiv:2412.04506)
• BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity (arXiv:2402.03216)
• BGE-en-ICL / BGE-ICL: Making Text Embedders Few-Shot Learners (arXiv:2409.15700)
• A Universal Framework for Compressing Embeddings in CTR Prediction (arXiv:2502.15355)

Glossary

| Term | Meaning | |---|---| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | nDCG | Normalized Discounted Cumulative Gain, a ranking quality metric | | Agentic Search | Search modeled as sequential decision-making and tool use | | Gen-IR | Generative Information Retrieval |

> Note: This entry is an annotated index item based on the paper's public metadata. Quantitative results should be verified against the original PDF.