Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking

• URL: http://arxiv.org/abs/2404.08535v1
• Date: Fri, 12 Apr 2024 15:30:03 GMT
• Title: Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
• Authors: Tianyu Zhu, Myong Chol Jung, Jesse Clark,
• Abstract summary: We propose Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking (GCL) GCL is designed to learn from fine-grained rankings beyond binary relevance scores. Our results show that GCL achieves a 94.5% increase in NDCG@10 for in-domain and 26.3 to 48.8% increases for cold-start evaluations.
• Score: 2.5238707656136694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular contrastive frameworks typically learn from binary relevance, making them ineffective at incorporating direct fine-grained rankings. In this paper, we curate a large-scale dataset featuring detailed relevance scores for each query-document pair to facilitate future research and evaluation. Subsequently, we propose Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking (GCL), which is designed to learn from fine-grained rankings beyond binary relevance scores. Our results show that GCL achieves a 94.5% increase in NDCG@10 for in-domain and 26.3 to 48.8% increases for cold-start evaluations, all relative to the CLIP baseline and involving ground truth rankings.

Related papers

• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning [17.83428132220955]
  We propose a pre-retrieval framework named Pseudo-Graph Retrieval-Augmented Generation (PG-RAG) PG-RAG conceptualizes LLMs as students by providing them with abundant raw reading materials. During the retrieval phase, PG-RAG mimics the human behavior in flipping through notes.
  arXiv  Detail & Related papers  (2024-05-27T08:26:45Z)
• Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset [48.708591046906896]
  Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks. We revisit and extend the available experiments on the Baidu-ULTR dataset. We find that standard unbiased learning-to-rank techniques robustly improve click predictions but struggle to consistently improve ranking performance.
  arXiv  Detail & Related papers  (2024-04-03T08:00:46Z)
• Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment [54.179859639868646]
  We propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories.
  arXiv  Detail & Related papers  (2023-07-27T07:42:44Z)
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting [65.00288634420812]
  Pairwise Ranking Prompting (PRP) is a technique to significantly reduce the burden on Large Language Models (LLMs) Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs.
  arXiv  Detail & Related papers  (2023-06-30T11:32:25Z)
• Learning to Rank when Grades Matter [11.981942948477236]
  Graded labels are ubiquitous in real-world learning-to-rank applications. Traditional learning-to-rank techniques ignore predicting actual grades. We propose a multiobjective formulation to jointly optimize both ranking and grade predictions.
  arXiv  Detail & Related papers  (2023-06-14T17:30:02Z)
• GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking [0.0]
  We propose a novel multi-label ranking method: GaussianMLR. It aims to learn implicit class significance values that determine the positive label ranks. We show that our method is able to accurately learn a representation of the incorporated positive rank order.
  arXiv  Detail & Related papers  (2023-03-07T14:09:08Z)
• Integrating Rankings into Quantized Scores in Peer Review [61.27794774537103]
  In peer review, reviewers are usually asked to provide scores for the papers. To mitigate this issue, conferences have started to ask reviewers to additionally provide a ranking of the papers they have reviewed. There are no standard procedure for using this ranking information and Area Chairs may use it in different ways. We take a principled approach to integrate the ranking information into the scores.
  arXiv  Detail & Related papers  (2022-04-05T19:39:13Z)
• GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks [68.61934077627085]
  We introduce GNNRank, a modeling framework compatible with any GNN capable of learning digraph embeddings. We show that our methods attain competitive and often superior performance compared with existing approaches.
  arXiv  Detail & Related papers  (2022-02-01T04:19:50Z)
• Query-level Early Exit for Additive Learning-to-Rank Ensembles [14.240566571342924]
  Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. In this paper, we investigate the novel problem of textitquery-level early exiting We show that query-level early exiting achieves an overall gain of up to 7.5% in terms of NDCG@10 with a speedup of the scoring process of up to 2.2x.
  arXiv  Detail & Related papers  (2020-04-30T08:59:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.