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Found 11 papers, 6 papers with code
Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models
With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval.
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation
First, it is hard to share the contextual information of the ranking list between the two tasks.
AI-Generated Images Introduce Invisible Relevance Bias to Text-Image Retrieval
Furthermore, our subsequent exploration reveals that the inclusion of AI-generated images in the training data of the retrieval models exacerbates the invisible relevance bias.
Plot Retrieval as an Assessment of Abstract Semantic Association
Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots.
Multi-task 3D building understanding with multi-modal pretraining
ULIP with PointNeXt and PointNeXt segmentation are extended for the classification and segmentation task on BuildingNet dataset.
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval
Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets.
Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks
This paper proposes a novel framework named \textbf{Search-in-the-Chain} (SearChain) for the interaction between LLM and IR to solve the challenges.
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework
Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, etc., while they share the same schema to estimate the relationship between texts.
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning
In generalization stage, matching model explores the essential matching signals by being trained on diverse matching tasks.
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.
