Tootfinder

This https://arxiv.org/abs/2404.06912 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers. But when re-rank\-ing multiple passages at once, existing cross-encoders inefficiently optimize the output ranking over several input permutations, as their passage interactions are not permutation-invariant. Moreover, their high memory footprint constrains the number of passages during listwise training. To tackle these issues, we propose the Set-Encoder, a new cross-encoder architecture that (1) introduces inter-passage attention with parallel …

Re-Ranking News Comments by Constructiveness and Curiosity Significantly Increases Perceived Respect, Trustworthiness, and Interest
Emily Saltz, Zaria Howard, Tin Acosta
https://arxiv.org/abs/2404.05429

Re-Ranking News Comments by Constructiveness and Curiosity Significantly Increases Perceived Respect, Trustworthiness, and Interest
Online commenting platforms have commonly developed systems to address online harms by removing and down-ranking content. An alternative, under-explored approach is to focus on up-ranking content to proactively prioritize prosocial commentary and set better conversational norms. We present a study with 460 English-speaking US-based news readers to understand the effects of re-ranking comments by constructiveness, curiosity, and personal stories on a variety of outcomes related to willingness to…

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Ferdinand Schlatt, Maik Fr\"obe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen
https://arxiv.org/abs/2404.06912

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers. But when re-rank\-ing multiple passages at once, existing cross-encoders inefficiently optimize the output ranking over several input permutations, as their passage interactions are not permutation-invariant. Moreover, their high memory footprint constrains the number of passages during listwise training. To tackle these issues, we propose the Set-Encoder, a new cross-encoder architecture that (1) introduces inter-passage attention with parallel …

Ranking NFL's top-12 WR trios heading into 2024 draft: Texans atop list after Stefon Diggs trade

https://www.cbssports.com/nfl/news/ranking

Ranking NFL's top-12 WR trios heading into 2024 draft: Texans atop list after Stefon Diggs trade
A re-ordering of the NFL's wide receiver trios after all the offseason action

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Ferdinand Schlatt, Maik Fr\"obe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen
https://arxiv.org/abs/2404.06912

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers. But when re-rank\-ing multiple passages at once, existing cross-encoders inefficiently optimize the output ranking over several input permutations, as their passage interactions are not permutation-invariant. Moreover, their high memory footprint constrains the number of passages during listwise training. To tackle these issues, we propose the Set-Encoder, a new cross-encoder architecture that (1) introduces inter-passage attention with parallel …

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
Yilin Wang, Minghao Hu, Zhen Huang, Dongsheng Li, Dong Yang, Xicheng Lu
https://arxiv.org/abs/2403.17532

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large language models (LLMs) have shown outstanding performance on several tasks such as information extraction and dialog systems. Leveraging them for KGC re-ranking is beneficial for leveraging the extensive pre-trained knowledge and powerful generative c…

Ranked List Truncation for Large Language Model-based Re-Ranking
Chuan Meng, Negar Arabzadeh, Arian Askari, Mohammad Aliannejadi, Maarten de Rijke
https://arxiv.org/abs/2404.18185

Ranked List Truncation for Large Language Model-based Re-Ranking
We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candidates). RLT is crucial for re-ranking as it can improve re-ranking efficiency by sending variable-length candidate lists to a re-ranker on a per-query basis. It also has the potential to improve re-ranking effectiveness. Despite its importance, there is limited research into applying RLT methods to this new perspective.…

This ranking biases toward places lots of people walk/bike to begin with. Tons of pedestrians/cyclists at Market & Octavia = tons of opportunities for injury.
Very few people walk or bike near 19th Ave & Sloat, but it still made the citywide top 10. So if you're wondering where, if *you* decide to walk there, you'll have the highest risk of getting hit, 19th Ave & Sloat is by far the most dangerous on the list.

This https://arxiv.org/abs/2403.05881 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a q…

I'd like to congratulate arts and humanities journals for continuing to flummox the folks at Clarivate by remaining less (quantitatively) rank-able, despite Clarivate's best efforts to put a number on everything, no matter the meaninglessness of the exercise. Stay weird arts and humanities!
#ScholarlyPublishing

2024 Journal Citation Reports: Changes in Journal Impact Factor category rankings to enhance transparency and inclusivity - Clarivate
The 2024 Journal Citation Reports coming in June will feature new unified rankings for each of our 229 science and social science categories; no Journal Impact Factor (JIF)™ rankings for the arts and humanities categories.

A Taxation Perspective for Fair Re-ranking
Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, Tat-Seng Chua
https://arxiv.org/abs/2404.17826 https://…

A Taxation Perspective for Fair Re-ranking
Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in economics, offering valuable insights for conceptualizing fair re-ranking as a taxation process. Such a formulation provides us with a fresh perspective to re-examine fair re-ranking and inspire the development of new methods. From a taxation perspective, we theoretically demonstrate that most previous fair re-ranking…

This https://arxiv.org/abs/2305.14685 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we propose a novel re-ranker Fusion-in-T5 (FiT5), which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention. Experiments on passage ranking benchmarks MS MARCO and TREC DL show that FiT5, as one single model, sign…

Join Zain Hasan at this year's #bbuzz as he discusses advanced retrieval augmented generation (RAG) techniques such as hybrid search, re-ranking, query generation, and semantic text chunking.
https://2024.berli…

This https://arxiv.org/abs/2310.09342 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Ranking LLM-Generated Loop Invariants for Program Verification
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants. This can lead to a large number of calls to a program verifier to establish an invariant. To address this issue, we propose a {\it re-ranking} approach for the generated results…

Sports Illustrated says the Las Vegas Raiders made one of the best signings during the first wave of NFL free agency https://www.yardbarker.com/nfl/articles/spo…

Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023
Andrew Parry, Thomas Jaenich, Sean MacAvaney, Iadh Ounis
https://arxiv.org/abs/2405.01122

Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023
This paper describes our participation in the TREC 2023 Deep Learning Track. We submitted runs that apply generative relevance feedback from a large language model in both a zero-shot and pseudo-relevance feedback setting over two sparse retrieval approaches, namely BM25 and SPLADE. We couple this first stage with adaptive re-ranking over a BM25 corpus graph scored using a monoELECTRA cross-encoder. We investigate the efficacy of these generative approaches for different query types in first-st…

Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations
Xiaoqing Zhang, Xiuying Chen, Shen Gao, Shuqi Li, Xin Gao, Ji-Rong Wen, Rui Yan
https://arxiv.org/abs/2404.04272

Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations
Information-seeking dialogue systems are widely used in e-commerce systems, with answers that must be tailored to fit the specific settings of the online system. Given the user query, the information-seeking dialogue systems first retrieve a subset of response candidates, then further select the best response from the candidate set through re-ranking. Current methods mainly retrieve response candidates based solely on the current query, however, incorporating similar questions could introduce m…

This https://arxiv.org/abs/2404.09709 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), whi…

A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE
Herv\'e D\'ejean, St\'ephane Clinchant, Thibault Formal
https://arxiv.org/abs/2403.10407

A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE
We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers. We conduct a large evaluation on TREC Deep Learning datasets and out-of-domain datasets such as BEIR and LoTTE. In the first set of experiments, we show how cross-encoder rerankers are hard to distinguish when it comes to re-rerank SPLADE on MS MARCO. Observations shift in the out-of-domain scenario, where both the type of model and the number of documents to re-rank…

This https://arxiv.org/abs/2312.17649 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Investigating the Effects of Sparse Attention on Cross-Encoders
Cross-encoders are effective passage and document re-rankers but less efficient than other neural or classic retrieval models. A few previous studies have applied windowed self-attention to make cross-encoders more efficient. However, these studies did not investigate the potential and limits of different attention patterns or window sizes. We close this gap and systematically analyze how token interactions can be reduced without harming the re-ranking effectiveness. Experimenting with asymmetr…

Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang
https://arxiv.org/abs/2404.09709

Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), whi…