Papers with zero-shot retrieval
Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries (2025.findings-acl)
Copied to clipboard
Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang, Weijia Lu, Xiaodong Zhang, Zhifei Yang, Xiaofeng Ma, Yanghua Xiao, Deqing Yang
| Challenge: | Current dense retrieval methods compute similarities between dense vectors but overlook the real query intents. |
| Approach: | They propose a neuro-symbolic information retrieval method that leverages first-order logic to optimize the embeddings of naive natural language by considering the logical consistency between queries and documents. |
| Outcome: | The proposed method outperforms existing methods on negative-constraint queries under zero-shot and low-resource retrieval tasks. |
Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)
Copied to clipboard
| Challenge: | Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document. |
| Approach: | They propose an unsupervised approach that performs unsupervised adaptation of large language models for dense retrieval. |
| Outcome: | The proposed model improves on a variety of dense retrieval benchmarks and is available on github. |
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies found that CLIP can only encode one aspect of the feature space, leading to substantial information loss and indistinctive features. |
| Approach: | They propose a model-agnostic approach that fine-tunes complementary CLIP models and transforms them into a CLIP-MoE. |
| Outcome: | The proposed framework fine-tunes a series of complementary CLIP models and transforms them into a CLIP-MoE. |
SQUARE: Unsupervised Retrieval Adaptation via Synthetic Data (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained retrieval models often face challenges in zero-shot retrieval for knowledge-based question answering . |
| Approach: | SQUARE is a method for corpus-specific unsupervised retrieval customization . it generates synthetic question-answer pairs from the corpus and fine-tunes it . |
| Outcome: | SQUARE is a new method for corpus-specific unsupervised retrieval customization. |
GENRA: Enhancing Zero-shot Retrieval with Rank Aggregation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been shown to perform zero-shot document retrieval, a process that typically consists of two steps: retrieving relevant documents, and re-ranking them based on their relevance to the query. |
| Approach: | They propose a new approach to zero-shot document retrieval that incorporates rank aggregation to improve retrieval effectiveness. |
| Outcome: | The proposed approach improves existing methods on benchmark datasets and shows that it can perform zero-shot retrieval. |
HIL: Hybrid Isotropy Learning for Zero-shot Performance in Dense retrieval (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in dense retrieval models have brought ColBERT to prominence in information retrieval, but it is underperforming in zero-shot tasks. |
| Approach: | They propose a Hybrid Isotropy Learning architecture that integrates isotropic and anisotropic representations to improve zero-shot retrieval performance. |
| Outcome: | The proposed model outperforms the baseline ColBERT model in BEIR benchmarks. |
Zero-Shot Multimodal Retrieval with Multi-Scale Contextual Representations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multimodal information retrieval (MMIR) lack generalization across different modalities and require annotated training data. |
| Approach: | They propose a fine-tuning-free, two-stage MMIR approach that couples efficient candidate filtering with fine-grained multimodal re-ranking. |
| Outcome: | The proposed approach outperforms supervised methods on 23 datasets. |
Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)
Copied to clipboard
Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang
| Challenge: | Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue. |
| Approach: | They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates. |
| Outcome: | The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets. |
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)
Copied to clipboard
Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting. |
| Approach: | They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset. |
| Outcome: | The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting. |
Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models have demonstrated that explicit step-by-step thinking can substantially improve performance on complex tasks. |
| Approach: | They propose a model that generates preliminary thoughts for input queries before document retrieval. |
| Outcome: | The proposed model generates preliminary thoughts for input queries before document retrieval. |