DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models fail to handle the varied search intents inherent to disaster management scenarios, resulting in inconsistent and unreliable performance. |
| Approach: | They propose a new series of dense retrieval models tailored for disaster management that train on a three-stage framework with unsupervised contrastive pre-training and difficulty-aware progressive instruction fine-tuning. |
| Outcome: | The proposed model outperforms baseline models by 13.3 times and 33 times over baselines with only 7.6% of their parameters. |
Similar Papers
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
Copied to clipboard
Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)
Copied to clipboard
Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
| Challenge: | a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy. |
| Approach: | They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition . |
| Outcome: | The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks. |
Making Large Language Models Efficient Dense Retrievers (2026.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that fine-tuning large language models for dense retrieval yields strong performance, but their substantial parameter counts make them computationally inefficient. |
| Approach: | They propose a framework for developing efficient retrievers that performs coarse-to-fine compression through a coarse-grained coarse-tuning strategy. |
| Outcome: | The proposed framework reduces model size and inference cost while preserving performance of full-size models. |
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval (2024.emnlp-main)
Copied to clipboard
| Challenge: | a conversational search system requires accurate interpretation of user intent from complex multi-turn contexts. |
| Approach: | They propose a dual-learning approach that adapts LLMs for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning. |
| Outcome: | The proposed approach outperforms existing retrieval methods on five conversational search benchmarks. |
CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)
Copied to clipboard
Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan
| Challenge: | Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context. |
| Approach: | They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus. |
| Outcome: | The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity. |
CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)
Copied to clipboard
Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang
| Challenge: | Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries . |
| Approach: | They propose a framework that facilitates the coevolution of large language models and retrieval models. |
| Outcome: | The proposed framework facilitates the coevolution of LLMs and retrieval models. |
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG (2025.findings-naacl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is widely adopted in Large Language Models, but is flat and has limitations such as a significant burden on one retriever and constant granularity limits the ceiling of retrieval performance. |
| Approach: | They propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency. |
| Outcome: | The proposed paradigm achieves comparable retrieval performance while the time overhead is reduced by nearly 40%. |
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval (2022.coling-1)
Copied to clipboard
| Challenge: | Recent studies show that prompt tuning is unfriendly for industrial deployment in dense retrieval tasks. |
| Approach: | They propose to apply prompt tuning to dense retrieval tasks to reduce deployment cost . they propose to use retrieval-oriented intermediate pretraining and unified negative mining . |
| Outcome: | The proposed method outperforms state-of-the-art models on MS-MARCO and Natural Questions. |
LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Using retrieval augmentation, large vision language models can be used for diagnostic accuracy, but multimodal retrieval-augmented diagnosis is challenging. |
| Approach: | They propose a lightweight mechanism for enhancing diagnostic performance of retrieval-augmented LVLMs by fine-tuning a multimodal retriever and general-purpose backbone models. |
| Outcome: | The proposed mechanism achieves competitive results without medical training compared to pre-trained models with extensive training. |
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (2024.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models have limited generalization capabilities and performance challenges. |
| Approach: | They evaluate 15 different backbone LLMs and non-LLMs to evaluate their performance . larger models and extensive pre-training consistently enhance in-domain accuracy and data efficiency . |
| Outcome: | The results show that larger models and extensive pre-training enhance in-domain accuracy and data efficiency. |