zELO: ELO-inspired Training Method for Rerankers and Embedding Models

We introduce a novel training methodology named zELO, which optimizes retrieval performance via the analysis that ranking tasks are statically equivalent to a Thurstone model. Based on the zELO method, we use unsupervised data in order train a suite of state-of-the-art open-weight reranker models: zerank-1 and zerank-1-small. These models achieve the highest retrieval scores in multiple domains, including finance, legal, code, and STEM, outperforming closed-source proprietary rerankers on both NDCG@10 and Recall. These models also demonstrate great versatility, maintaining their 0-shot performance on out-of-domain and private customer datasets. The training data included 112,000 queries and 100 documents per query, and was trained end-to-end from unannotated queries and documents in less than 10,000 H100-hours.

LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain

Retrieval-Augmented Generation (RAG) systems are showing promising potential, and are becoming increasingly relevant in AI-powered legal applications. Existing benchmarks, such as LegalBench, assess the generative capabilities of Large Language Models (LLMs) in the legal domain, but there is a critical gap in evaluating the retrieval component of RAG systems. To address this, we introduce LegalBench-RAG, the first benchmark specifically designed to evaluate the retrieval step of RAG pipelines within the legal space. LegalBench-RAG emphasizes precise retrieval by focusing on extracting minimal, highly relevant text segments from legal documents. These highly relevant snippets are preferred over retrieving document IDs, or large sequences of imprecise chunks, both of which can exceed context window limitations. Long context windows cost more to process, induce higher latency, and lead LLMs to forget or hallucinate information. Additionally, precise results allow LLMs to generate citations for the end user. The LegalBench-RAG benchmark is constructed by retracing the context used in LegalBench queries back to their original locations within the legal corpus, resulting in a dataset of 6,858 query-answer pairs over a corpus of over 79M characters, entirely human-annotated by legal experts. We also introduce LegalBench-RAG-mini, a lightweight version for rapid iteration and experimentation. By providing a dedicated benchmark for legal retrieval, LegalBench-RAG serves as a critical tool for companies and researchers focused on enhancing the accuracy and performance of RAG systems in the legal domain. The LegalBench-RAG dataset is publicly available at https://github.com/zeroentropy-cc/legalbenchrag.