arXiv AI
Jun 2
Test-Time Training for Zero-Resource Dense Retrieval Reranking
arXiv:2606. 01070v1 Announce Type: cross Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings.
Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking
arXiv:2607. 11933v1 Announce Type: cross Abstract: Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment.
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arXiv Computation and Language
Sep 23
KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
arXiv:2606.22807v3 Announce Type: replace Abstract: As retrieval systems scale, effective and efficient reranking becomes increasingly important. However, most existing encoder- and decoder-based rer...
Hugging Face Trending Papers
Jun 22
KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
As retrieval systems scale, high-quality reranking becomes increasingly important. However, most existing rerankers, whether encoder-based or decoder-based, jointly encode the query and passage, tightly coupling their computation and limiting deployment efficiency as well as flexibility.
arXiv Machine Learning
Jun 15
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.
arXiv AI
Sep 17
One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
The paper introduces DRAG, a query‑adaptive framework that jointly selects retriever and generator configurations for Retrieval‑Augmented Generation (RAG) systems. Two variants are presented: DRAG_QPP, a training‑free routing method using Query Performance Prediction and perplexity signals, and DRAG_SFT, a supervised approach that fine‑tunes an LLM to predict configurations. Experiments on three LLM families and four QA benchmarks show that DRAG_QPP matches strong static baselines while cutting inference latency, and DRAG_SFT consistently outperforms both static and training‑free adaptive baselines, demonstrating a better effectiveness‑efficiency trade‑off.
arXiv AI
Aug 5
SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
arXiv:2608. 03860v1 Announce Type: cross Abstract: We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19.
arXiv AI
Aug 18
What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering
arXiv:2608. 14841v1 Announce Type: new Abstract: Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typically follows retrieve-then-read pipelines.
arXiv AI
Sep 10
Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment
arXiv:2609.08188v1 Announce Type: new Abstract: Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retriever...
arXiv AI
Aug 25
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
The paper introduces LLM-QL, a dense retrieval model that harnesses large language models (LLMs) by maximizing query likelihood (QL) as an auxiliary task. It incorporates an Attention Block to limit predictive token attention to document tokens before the ending token and a Document Corruption component that masks parts of the document during prediction. Experiments on MS MARCO and BEIR datasets show that LLM-QL outperforms other LLM-based retrievers, and detailed analyses confirm the effectiveness of its components.
arXiv AI
Aug 11
TreeHop: Efficient Embedding-Level Query Rewriter
arXiv:2504. 20114v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks.
arXiv AI
Jul 3
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
arXiv Machine Learning
Sep 22
AdaMem: Adaptive Memory Token Allocation for Soft Compression in Retrieval-Augmented Generation
arXiv:2609.22100v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves language models with retrieved evidence, but processing many long passages is costly and can introduce...