arXiv AI

From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search

arXiv Computation and Language

Oct 22

TAGR: Task-Adaptive Geo-Semantic Retrieval across Heterogeneous Urban Delivery Tasks

arXiv:2610.28638v1 Announce Type: cross Abstract: As urban residents demand higher-quality instant delivery services, demand forecasting, estimated time of arrival prediction, and next-stop predictio...

By Zhi Liu, Yurun Cai, Xiao Han, Zhi Zhen, Yuran Li, Guojiang Shen, Xiangjie Kong

llms benchmarks

arXiv AI

Sep 15

Attention Calibration for Position-Fair Dense Retrieval

arXiv:2606.02737v2 Announce Type: replace-cross Abstract: Dense retrieval compresses a passage into a single vector, but this compression is positionally skewed: early content dominates the embedding...

By Andrianos Michail, Elias Schuhmacher, Juri Opitz, Simon Clematide, Rico Sennrich

llms rag benchmarks

arXiv AI

Jul 31

SkillSight: Calibrating Generic Content Bias for Skill Retrieval

arXiv:2607. 18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.

By Jinying Xiao, Bin Li, Xiaopeng Li, Jianling Li, Jiacheng Jie, Xiaodong Liu, Ma Jun, Chao Wang, Nyima Tashi, Jie Yu

llms agents safety

arXiv AI

Jul 16

With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots

arXiv:2602. 09616v2 Announce Type: replace-cross Abstract: Reliable retrieval-augmented generation (RAG) systems depend fundamentally on the retriever's ability to find relevant information.

By Zeinab Sadat Taghavi, Ali Modarressi, Hinrich Schutze, Andreas Marfurt

rag

arXiv Computation and Language

Sep 22

URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER

arXiv:2609.24372v1 Announce Type: new Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limi...

By Jingyu Wang, Shijie Wu, Fusheng Jin

llms rag nlp benchmarks safety

arXiv AI

Jul 22

SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval

arXiv:2607. 18785v1 Announce Type: new Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.

By Jinying Xiao, Bin Ji, Shasha Li, Xiaodong Liu, Ma Jun, Jiacheng Jie, Chao Wang, Nyima Tashi, Jie Yu

llms agents safety

arXiv Computation and Language

Oct 15

When More Documents Hurt RAG: Measuring and Reducing Vector Search Dilution with Domain-Scoped Retrieval

arXiv:2606.11350v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) degrades when applied to large, heterogeneous document collections. In RAG systems, dense similarity becomes l...

By Nabaraj Subedi, Ahmed Abdelaty, Shivanand Venkanna Sheshappanavar

llms rag agents benchmarks

arXiv Computation and Language

Oct 16

Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework

The paper introduces CE‑RAG4EM, a cost‑efficient framework that applies retrieval‑augmented generation (RAG) to entity matching (EM) by using blocking‑based batch retrieval and generation. It demonstrates that, in a zero‑shot setting, CE‑RAG4EM achieves comparable or better matching quality than naive RAG while significantly reducing end‑to‑end overhead. Experiments on nine real‑world EM benchmarks show that the approach closes the knowledge grounding gap for LLM‑based EM, especially on datasets with ambiguous or sparse attributes, and provides clear guidance on balancing accuracy and computational cost through blocking size and retrieval granularity.

By Chuangtao Ma, Zeyu Zhang, Arijit Khan, Sebastian Schelter, Paul Groth

llms rag benchmarks

arXiv AI

Sep 1

SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents

arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...

By Hongcheol Cho, Ryangkyung Kang, Youngeun Kim

llms rag agents fine-tuning benchmarks

arXiv Computation and Language

Aug 31

Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection

The paper investigates modular entity disambiguation by separating candidate retrieval from entity selection. It compares sparse retrieval (BM25), Web KB search, and a dense retriever, all paired with large language model selectors. Results show that a training‑free BM25 retriever combined with an LLM selector achieves state‑of‑the‑art performance on the ZELDA benchmark, and the modular approach enables abstention when retrieval fails.

By Fina Polat, Daniel Daza, Pengyu Zhang, Klim Zaporojets, Paul Groth

llms rag benchmarks

arXiv Machine Learning

Sep 22

Guiding the coarse levels of semantic IDs makes the fine levels learnable

arXiv:2609.22227v1 Announce Type: cross Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...

By Bin Wang, Zhengyu Zhang

llms rag nlp reinforcement-learning safety

arXiv Machine Learning

Jul 28

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.

By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

llms rag fine-tuning benchmarks

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