reranking

This paper presents an experimental study on using AI to find simulation models via natural language queries, evaluating data representations, embedding models, and retrieval strategies, finding that open-source embeddings and reranking methods significantly improve performance.

This paper challenges the assumption that reranking always improves few-shot selection for LLMs, proposing a training-free gated reranking approach that uses model uncertainty to decide when to rerank, reducing computational costs by 15-80% while slightly improving performance.

This paper presents 5ting, a system for multi-turn Retrieval-Augmented Generation (RAG) that combines BGE-M3 dense retrieval, FAISS indexing, LLM-based reranking, and evidence-constrained generation. The system achieves strong results on SemEval-2026 Task 8, with a retrieval nDCG@5 of 0.4719 and an end-to-end harmonic score of 0.5597.

KaLM-Reranker-V1 is a fast reranker that decouples query and passage computation using an encoder-decoder architecture with Matryoshka embedding pooling and cross-attention, achieving state-of-the-art reranking performance on BEIR and competitive results on multilingual benchmarks.

This paper introduces ADAS, a training-free reranking rule for parallel masked diffusion decoding that uses attention to discount tokens that strongly attend to uncertain positions, improving low-NFE performance on reasoning and code tasks with minimal runtime overhead.

The paper proposes SF-Re2G, a method that improves document-grounded dialogue systems by leveraging document structure to enhance retrieval, reranking, and generation. It validates on Chinese and English datasets.

A practical fix for RAG hallucination caused by noisy retrieval: use cross-encoder re-ranking to filter chunks with a score > 1.5, improving relevance from -0.28 to +3.80 on average.

Garry Tan released new gbrain-evals benchmarks showing ZeroEntropy.dev achieves state-of-the-art performance in reranking and embedding cost, speed, and retrieval success, beating MemPalace and Vector RAG.

Proposes reframing Pairwise Ranking Prompting (PRP) reranking as active learning from noisy pairwise comparisons, improving NDCG@10 per call under budget constraints, and introduces a randomized-direction oracle that reduces LLM calls per pair.

This paper reframes pairwise ranking prompting as active learning from noisy comparisons, introducing a noise-robust framework with a randomized-direction oracle to improve ranking quality under call constraints and address position bias.

This paper describes a system for SemEval-2026 Task 8 that uses a three-stage pipeline involving query rewriting with a fine-tuned Qwen model, hybrid retrieval, and cross-encoder reranking to improve multi-turn retrieval performance.

MemReranker is a reasoning-aware reranking model family (0.6B/4B) designed for agent memory retrieval, addressing limitations in semantic similarity by incorporating LLM knowledge distillation for better temporal and causal reasoning.

This paper introduces CoREB, a contamination-limited multitask benchmark for code search that evaluates text-to-code, code-to-text, and code-to-code retrieval with fine-tuned reranking capabilities.

Researchers identify systematic English and query-language bias in multilingual RAG rerankers and introduce LAURA, a utility-driven alignment method that boosts performance by retrieving answer-critical documents across languages.

Sentence Transformers v5.4 introduces support for multimodal embedding and reranking, allowing users to encode and compare text, images, audio, and video using a unified API.