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Found 24 papers, 10 papers with code
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning
Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge.
When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation
This motivates us to enhance the LLMs' ability to perceive their knowledge boundaries to help RA.
Reproducibility Analysis and Enhancements for Multi-Aspect Dense Retriever with Aspect Learning
MADRAL was evaluated on proprietary data and its code was not released, making it challenging to validate its effectiveness on other datasets.
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e. g., products with aspects such as category and brand.
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently.
CIR at the NTCIR-17 ULTRE-2 Task
The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task.
A Comparative Study of Training Objectives for Clarification Facet Generation
In this paper, we aim to conduct a systematic comparative study of various types of training objectives, with different properties of not only whether it is permutation-invariant but also whether it conducts sequential prediction and whether it can control the count of output facets.
L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations
First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection.
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings.
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search
Then we fine-tune several pre-trained models and train an ensemble model to aggregate all the predictions from various pre-trained models with human-annotation data in the fine-tuning stage.
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker.
Asking Clarifying Questions Based on Negative Feedback in Conversational Search
To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns.
Leveraging User Behavior History for Personalized Email Search
Given these observations, we propose to leverage user search history as query context to characterize users and build a context-aware ranking model for email search.
A Transformer-based Embedding Model for Personalized Product Search
Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user's purchase history with a transformer architecture.
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
We describe the annotation process in detail and compare it with other similar evaluation systems.
Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search
RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence.
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
Explainable Product Search with a Dynamic Relation Embedding Model
Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session.
A Study of Context Dependencies in Multi-page Product Search
In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
Conversational Product Search Based on Negative Feedback
So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.
Leverage Implicit Feedback for Context-aware Product Search
However, customers with little or no purchase history do not benefit from personalized product search.
Revisiting Iterative Relevance Feedback for Document and Passage Retrieval
We conduct extensive experiments to analyze and compare IRF with the standard top-k RF framework on document and passage retrieval.
Learning a Deep Listwise Context Model for Ranking Refinement
Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results.
Unbiased Learning to Rank with Unbiased Propensity Estimation
We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.
