E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search
cs.CL, cs.AI, cs.IR
Submitted: 2025-10-26
Updated: 2026-08-26
Comments: Accepted by EMNLP 2026 main conference. Code and models are avaliable at https://alibaba-nlp.github.io/E2Rank
Project page: https://alibaba-nlp.github.io/E2Rank
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking
- M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
- Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
- TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
- Overview of the TREC 2019 deep learning track
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Unsupervised Dense Information Retrieval with Contrastive Learning
- Making Text Embedders Few-Shot Learners
- Towards General Text Embeddings with Multi-stage Contrastive Learning
- Holistic Evaluation of Language Models
- LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
- Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models
- DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task
- ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability
- Fine-Tuning LLaMA for Multi-Stage Text Retrieval
- MTEB: Massive Text Embedding Benchmark
- Generative Representational Instruction Tuning
- Large Dual Encoders Are Generalizable Retrievers
- JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
- Multi-Stage Document Ranking with BERT
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