Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance

• URL: http://arxiv.org/abs/2503.23239v2
• Date: Tue, 04 Nov 2025 07:25:08 GMT
• Title: Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance
• Authors: Reza Esfandiarpoor, George Zerveas, Ruochen Zhang, Macton Mgonzo, Carsten Eickhoff, Stephen H. Bach,
• Abstract summary: In this work, we forgo real documents and annotations and use large language models to generate synthetic documents.<n>Our experiments on MS MARCO and BEIR benchmark show that our proposed approach outperforms conventional training with InfoNCE by a large margin.
• Score: 30.879299174443812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although synthetic data has changed various aspects of information retrieval (IR) pipelines, the main training paradigm remains: contrastive learning with binary relevance labels, where one positive document is compared against several negatives using the InfoNCE loss. This objective treats all documents that are not explicitly annotated as relevant on an equally negative footing, regardless of their actual degree of relevance, thus missing subtle nuances useful for ranking. To overcome this limitation, in this work, we forgo real documents and annotations and use large language models to directly generate synthetic documents that answer the MS MARCO queries according to several different levels of relevance. We also propose using Wasserstein distance as a more effective loss function for training transformer-based retrievers with graduated relevance labels. Our experiments on MS MARCO and BEIR benchmark show that our proposed approach outperforms conventional training with InfoNCE by a large margin. Without using any real documents, our method significantly improves self-supervised retrievers and is more robust to distribution shift compared to contrastive learning using real data. Our method also successfully integrates existing real data into the synthetic ranking context, further boosting the performance. Overall, we show that generating multi-level ranking contexts is a better approach to synthetic data generation for IR than just generating the standard positive and negative documents.

Related papers

• Can LLM Annotations Replace User Clicks for Learning to Rank? [112.2254432364736]
  Large-scale supervised data is essential for training modern ranking models, but obtaining high-quality human annotations is costly.<n>Click data has been widely used as a low-cost alternative, and with recent advances in large language models (LLMs), LLM-based relevance annotation has emerged as another promising annotation.<n> Experiments on both a public dataset, TianGong-ST, and an industrial dataset, Baidu-Click, show that click-supervised models perform better on high-frequency queries.<n>We explore two training strategies -- data scheduling and frequency-aware multi-objective learning -- that integrate both supervision signals.
  arXiv  Detail & Related papers  (2025-11-10T02:26:14Z)
• Enhancing Transformer-Based Rerankers with Synthetic Data and LLM-Based Supervision [0.13999481573773073]
  Large Language Models (LLMs) excel at reranking due to their deep semantic understanding and reasoning.<n>Fine-tuning smaller, task-specific models is a more efficient alternative but typically on scarce, manually labeled data.<n>We propose a novel pipeline that eliminates the need for human-labeled query-document pairs.
  arXiv  Detail & Related papers  (2025-09-23T09:47:27Z)
• Improving Document Retrieval Coherence for Semantically Equivalent Queries [63.97649988164166]
  We propose a variation of the Multi-Negative Ranking loss for training DR that improves the coherence of models in retrieving the same documents.<n>The loss penalizes discrepancies between the top-k ranked documents retrieved for diverse but semantic equivalent queries.
  arXiv  Detail & Related papers  (2025-08-11T13:34:59Z)
• BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation [6.272555849379284]
  BiXSE is a pointwise training method that optimize binary cross-entropy over graded relevance scores.<n>It achieves strong performance with reduced annotation and compute costs.<n>BiXSE offers a robust, scalable alternative for training dense retrieval models.
  arXiv  Detail & Related papers  (2025-08-09T02:15:17Z)
• Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data [43.81779293196647]
  We investigate improving the retrieval effectiveness of embedding models through the lens of corpus-specific fine-tuning.<n>We find that fine-tuning using the conventional InfoNCE contrastive loss often reduces effectiveness in state-of-the-art models.<n>We use our approach to train an embedding model that achieves state-of-the-art effectiveness among BERT embedding models.
  arXiv  Detail & Related papers  (2025-05-25T19:06:19Z)
• ReasonIR: Training Retrievers for Reasoning Tasks [139.54343970560103]
  ReasonIR-8B is the first retriever specifically trained for general reasoning tasks. It achieves a new state-of-the-art of 29.9 nDCG@10 without reranker and 36.9 nDCG@10 with reranker on BRIGHT, a widely-used information retrieval benchmark.
  arXiv  Detail & Related papers  (2025-04-29T09:49:28Z)
• Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models [51.608246558235166]
  SCARLet is a framework for training utility-based retrievers in RALMs. It incorporates two key factors, multi-task generalization and inter-passage interaction. We evaluate our approach on ten datasets across various tasks, both in-domain and out-of-domain.
  arXiv  Detail & Related papers  (2025-04-01T09:28:28Z)
• Contrastive Learning to Improve Retrieval for Real-world Fact Checking [84.57583869042791]
  We present Contrastive Fact-Checking Reranker (CFR), an improved retriever for fact-checking complex claims. We leverage the AVeriTeC dataset, which annotates subquestions for claims with human written answers from evidence documents. We find a 6% improvement in veracity classification accuracy on the dataset.
  arXiv  Detail & Related papers  (2024-10-07T00:09:50Z)
• PairDistill: Pairwise Relevance Distillation for Dense Retrieval [35.067998820937284]
  This paper introduces Pairwise Relevance Distillation (PairDistill) to leverage pairwise reranking. It offers fine-grained distinctions between similarly relevant documents to enrich the training of dense retrieval models. Our experiments demonstrate that PairDistill outperforms existing methods, achieving new state-of-the-art results across multiple benchmarks.
  arXiv  Detail & Related papers  (2024-10-02T09:51:42Z)
• Noisy Self-Training with Synthetic Queries for Dense Retrieval [49.49928764695172]
  We introduce a novel noisy self-training framework combined with synthetic queries. Experimental results show that our method improves consistently over existing methods. Our method is data efficient and outperforms competitive baselines.
  arXiv  Detail & Related papers  (2023-11-27T06:19:50Z)
• Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training [81.3781338418574]
  We propose relevance-aware contrastive learning. We consistently improve the SOTA unsupervised Contriever model on the BEIR and open-domain QA retrieval benchmarks. Our method can not only beat BM25 after further pre-training on the target corpus but also serves as a good few-shot learner.
  arXiv  Detail & Related papers  (2023-06-05T18:20:27Z)
• Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking [56.80065604034095]
  We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
  arXiv  Detail & Related papers  (2022-10-19T16:19:37Z)
• On the Transferability of Pre-trained Language Models: A Study from Artificial Datasets [74.11825654535895]
  Pre-training language models (LMs) on large-scale unlabeled text data makes the model much easier to achieve exceptional downstream performance. We study what specific traits in the pre-training data, other than the semantics, make a pre-trained LM superior to their counterparts trained from scratch on downstream tasks.
  arXiv  Detail & Related papers  (2021-09-08T10:39:57Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval [19.723551683930776]
  Interactive Information Retrieval (IIR) and Reinforcement Learning (RL) share many commonalities, including an agent who learns while interacting. To successfully apply RL methods to IIR, one challenge is to obtain sufficient relevance labels to train the RL agents. Our paper addresses this issue by using domain randomization to synthesize more relevant documents for the training.
  arXiv  Detail & Related papers  (2020-06-05T00:38:39Z)

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.