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- research-articleAugust 2024
- research-articleAugust 2024
Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 6Article No.: 149, Pages 1–26https://doi.org/10.1145/3662732Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-...
- research-articleAugust 2024JUST ACCEPTED
Federated Recommender System Based on Diffusion Augmentation and Guided Denoising
ACM Transactions on Information Systems (TOIS), Just Accepted https://doi.org/10.1145/3688570Sequential recommender systems often struggle with accurate personalized recommendations due to data sparsity issues. Existing works use variational autoencoders and generative adversarial network methods to enrich sparse data. However, they often ...
- research-articleJuly 2024JUST ACCEPTED
Online and Offline Evaluation in Search Clarification
ACM Transactions on Information Systems (TOIS), Just Accepted https://doi.org/10.1145/3681786The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that ...
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- research-articleJuly 2024JUST ACCEPTED
A Self-Distilled Learning to Rank Model for Ad-hoc Retrieval
ACM Transactions on Information Systems (TOIS), Just Accepted https://doi.org/10.1145/3681784Learning to rank models are broadly applied in ad-hoc retrieval for scoring and sorting documents based on their relevance to textual queries. The generalizability of the trained model in the learning to rank approach, however, can have an impact on the ...
- research-articleJuly 2024JUST ACCEPTED
On Elastic Language Models
ACM Transactions on Information Systems (TOIS), Just Accepted https://doi.org/10.1145/3677375Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. While their large scales ensure capacity, they also hinder deployment. Knowledge distillation offers an ...
- research-articleJuly 2024JUST ACCEPTED
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction
ACM Transactions on Information Systems (TOIS), Just Accepted https://doi.org/10.1145/3676957Due to the extraordinarily large number of parameters, fine-tuning Large Language Models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications. To avoid fine-tuning, we can alternatively treat a LLM as a black-box (...
- research-articleJune 2024JUST ACCEPTED
ROGER: Ranking-oriented Generative Retrieval
ACM Transactions on Information Systems (TOIS), Just Accepted https://doi.org/10.1145/3603167In recent years, various dense retrieval methods have been developed to improve the performance of search engines with a vectorized index. However, these approaches require a large pre-computed index and have limited capacity to memorize all semantics in ...
- research-articleMay 2024
Passage-aware Search Result Diversification
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 136, Pages 1–29https://doi.org/10.1145/3653672Research on search result diversification strives to enhance the variety of subtopics within the list of search results. Existing studies usually treat a document as a whole and represent it with one fixed-length vector. However, considering that a long ...
- research-articleApril 2024
Listwise Generative Retrieval Models via a Sequential Learning Process
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 133, Pages 1–31https://doi.org/10.1145/3653712Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing GR models commonly employ maximum ...
- research-articleApril 2024
Revisiting Bag of Words Document Representations for Efficient Ranking with Transformers
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 114, Pages 1–27https://doi.org/10.1145/3640460Modern transformer-based information retrieval models achieve state-of-the-art performance across various benchmarks. The self-attention of the transformer models is a powerful mechanism to contextualize terms over the whole input but quickly becomes ...
- research-articleApril 2024
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 118, Pages 1–28https://doi.org/10.1145/3639818With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval ...
- research-articleApril 2024
Towards Effective and Efficient Sparse Neural Information Retrieval
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 116, Pages 1–46https://doi.org/10.1145/3634912Sparse representation learning based on Pre-trained Language Models has seen a growing interest in Information Retrieval. Such approaches can take advantage of the proven efficiency of inverted indexes and inherit desirable IR priors such as explicit ...
- research-articleApril 2024
Data Augmentation for Sample Efficient and Robust Document Ranking
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 119, Pages 1–29https://doi.org/10.1145/3634911Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. ...
- research-articleApril 2024
Efficient Neural Ranking Using Forward Indexes and Lightweight Encoders
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 117, Pages 1–34https://doi.org/10.1145/3631939Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency.
We propose Fast-Forward indexes—vector forward indexes ...
- research-articleApril 2024
Retrieval for Extremely Long Queries and Documents with RPRS: A Highly Efficient and Effective Transformer-based Re-Ranker
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 115, Pages 1–32https://doi.org/10.1145/3631938Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specifically designed Transformer models that can handle long input sequences ...
- research-articleApril 2024
Multi-grained Document Modeling for Search Result Diversification
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 126, Pages 1–22https://doi.org/10.1145/3652852Search result diversification plays a crucial role in improving users’ search experience by providing users with documents covering more subtopics. Previous studies have made great progress in leveraging inter-document interactions to measure the ...
- research-articleApril 2024
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 123, Pages 1–29https://doi.org/10.1145/3652599Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not ...
- research-articleApril 2024
Generalized Weak Supervision for Neural Information Retrieval
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 5Article No.: 121, Pages 1–26https://doi.org/10.1145/3647639Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can ...