Tootfinder

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen, Xi Ye
https://arxiv.org/abs/2506.09944

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Recent work has identified retrieval heads (Wu et al., 2025b), a subset of attention heads responsible for retrieving salient information in long-context language models (LMs), as measured by their copy-paste behavior in Needle-in-a-Haystack tasks. In this paper, we introduce QRHEAD (Query-Focused Retrieval Head), an improved set of attention heads that enhance retrieval from long context. We identify QRHEAD by aggregating attention scores with respect to the input query, using a handful of exa…

NR4DER: Neural Re-ranking for Diversified Exercise Recommendation
Xinghe Cheng, Xufang Zhou, Liangda Fang, Chaobo He, Yuyu Zhou, Weiqi Luo, Zhiguo Gong, Quanlong Guan
https://arxiv.org/abs/2506.06341

NR4DER: Neural Re-ranking for Diversified Exercise Recommendation
With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individuali…

LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model
Tao Sun, Oliver Liu, JinJin Li, Lan Ma
https://arxiv.org/abs/2508.05602 https://

LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model
Multimodal generative AI usually involves generating image or text responses given inputs in another modality. The evaluation of image-text relevancy is essential for measuring response quality or ranking candidate responses. In particular, binary relevancy evaluation, i.e., ``Relevant'' vs. ``Not Relevant'', is a fundamental problem. However, this is a challenging task considering that texts have diverse formats and the definition of relevancy varies in different scenarios. We find that Multim…

Re-ranking 2021 NFL Draft by position: Where do Trevor Lawrence, Micah Parsons fall? https://www.nytimes.com/athletic/6400472/2025/06/05/nfl-draft-2021-micah-parsons-trevor-lawrence/

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF
Jaeyoon Kim, Yoonki Cho, Taeyoung Kim, Sung-Eui Yoon

IRanker: Towards Ranking Foundation Model
Tao Feng, Zhigang Hua, Zijie Lei, Yan Xie, Shuang Yang, Bo Long, Jiaxuan You
https://arxiv.org/abs/2506.21638 htt…

IRanker: Towards Ranking Foundation Model
Ranking tasks are ubiquitous, encompassing applications such as recommendation systems, LLM routing, and item re-ranking. We propose to unify these tasks using a single ranking foundation model (FM), as it eliminates the need for designing different models for each specific ranking task. However, unlike general supervision tasks in LLMs, ranking tasks do not have clear labels for supervision, posing great challenges to developing a ranking FM. To overcome these challenges, we propose IRanker, a…

Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Mengzhao Wang, Boyu Tan, Yunjun Gao, Hai Jin, Yingfeng Zhang, Xiangyu Ke, Xiaoliang Xu, Yifan Zhu
https://arxiv.org/abs/2508.01405

Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Hybrid search, the integration of lexical and semantic retrieval, has become a cornerstone of modern information retrieval systems, driven by demanding applications like Retrieval-Augmented Generation (RAG). The architectural design space for these systems is vast and complex, yet a systematic, empirical understanding of the trade-offs among their core components--retrieval paradigms, combination schemes, and re-ranking methods--is critically lacking. To address this, and informed by our experi…

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
Arjun Rao, Hanieh Alipour, Nick Pendar
https://arxiv.org/abs/2506.00049

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
This paper presents a comparison of embedding models in tri-modal hybrid retrieval for Retrieval-Augmented Generation (RAG) systems. We investigate the fusion of dense semantic, sparse lexical, and graph-based embeddings, focusing on the performance of the MiniLM-v6 and BGE-Large architectures. Contrary to conventional assumptions, our results show that the compact MiniLM-v6 outperforms the larger BGE-Large when integrated with LLM-based re-ranking within our tri-modal hybrid framework. Experim…

Mixture of Encoders is a vector-native alternative that models both structured and unstructured data in a unified embedding space. Join Filip Makraduli as he introduces the method, demonstrates how it powers natural language search and real-time recommendations, and shares open-source tools and benchmarks for replacing complex hybrid stacks.
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Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
Pascal J. Sager, Ashwini Kamaraj, Benjamin F. Grewe, Thilo Stadelmann
https://arxiv.org/abs/2505.23250

Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
We present the methodology and results of the Deep Retrieval team for subtask 4b of the CLEF CheckThat! 2025 competition, which focuses on retrieving relevant scientific literature for given social media posts. To address this task, we propose a hybrid retrieval pipeline that combines lexical precision, semantic generalization, and deep contextual re-ranking, enabling robust retrieval that bridges the informal-to-formal language gap. Specifically, we combine BM25-based keyword matching with a F…

A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
Rahul Raja, Anshaj Vats, Arpita Vats, Anirban Majumder
https://arxiv.org/abs/2507.21117

A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
Recommender systems have traditionally followed modular architectures comprising candidate generation, multi-stage ranking, and re-ranking, each trained separately with supervised objectives and hand-engineered features. While effective in many domains, such systems face persistent challenges including sparse and noisy interaction data, cold-start problems, limited personalization depth, and inadequate semantic understanding of user and item content. The recent emergence of Large Language Model…

Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
Van-Hoang Le, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
https://arxiv.org/abs/2507.14619

Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
Large Language Models (LLMs) face significant challenges in specialized domains like law, where precision and domain-specific knowledge are critical. This paper presents a streamlined two-stage framework consisting of Retrieval and Re-ranking to enhance legal document retrieval efficiency and accuracy. Our approach employs a fine-tuned Bi-Encoder for rapid candidate retrieval, followed by a Cross-Encoder for precise re-ranking, both optimized through strategic negative example mining. Key innov…

QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
Youzheng Liu, Jiyan Liu, Xiaoman Xu, Taihang Wang, Yimin Wang, Ye Jiang
https://arxiv.org/abs/2506.17272

QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval. Initially, we evaluate the performance of several retrieval models and select the one that yields the best results for candidate retrieval. Next, we employ multiple re-ranking models to enhance the candidate results, with each model selecting the Top-10 outcomes. In the final stage, we utilize weighted voting to deter…

CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Junze Chen, Xinjie Yang, Cheng Yang, Junfei Bao, Zeyuan Guo, Yawen Li, Chuan Shi
https://arxiv.org/abs/2506.17281

CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool. A common approach is using graph neural networks (GNNs) to capture high-order interaction relationships. As large language models (LLMs) have shown strong capabilities across domains, researchers are exploring their use to enhance recommendation. However, prior work limits LLMs to re-ranking results or dataset augmentation, failing to utilize their power during candidate filtering…

RMIT-ADM S at the SIGIR 2025 LiveRAG Challenge
Kun Ran, Shuoqi Sun, Khoi Nguyen Dinh Anh, Damiano Spina, Oleg Zendel
https://arxiv.org/abs/2506.14516 https…

RMIT-ADM+S at the SIGIR 2025 LiveRAG Challenge
This paper presents the RMIT--ADM+S participation in the SIGIR 2025 LiveRAG Challenge. Our Generation-Retrieval-Augmented Generation (GRAG) approach relies on generating a hypothetical answer that is used in the retrieval phase, alongside the original question. GRAG also incorporates a pointwise large language model (LLM)-based re-ranking step prior to final answer generation. We describe the system architecture and the rationale behind our design choices. In particular, a systematic evaluation…