| Challenge: | Existing methods for document classification do not consider document filtering . existing methods do not include document filter. |
| Approach: | They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds . |
| Outcome: | The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words . |
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Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
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| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
Precise Zero-Shot Dense Retrieval without Relevance Labels (2023.acl-long)
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| Challenge: | Existing dense retrieval systems that use semantic embedding similarities can be effective across tasks and languages. |
| Approach: | They propose to pivot through Hypothetical Document Embeddings (HyDE) given a query, HyDE first zero-shot prompts an instruction-following language model to generate a hypothetical document. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across tasks and languages. |
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) enable zero-shot approaches in open domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. |
| Approach: | They propose to use a distraction-aware answer selection framework to mitigate the impact of irrelevant documents in the retrieved set and the overconfidence of the generated answers to enhance the performance of zero-shot readers. |
| Outcome: | The proposed approach handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. |
A Representation Sharpening Framework for Zero Shot Dense Retrieval (2026.eacl-long)
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| Challenge: | Zero-shot dense retrieval requires generic, pretrained DRs, which struggle to represent semantic differences between similar documents. |
| Approach: | They propose a training-free representation sharpening framework that augments a document’s representation with information that helps differentiate it from similar documents in the corpus. |
| Outcome: | The proposed framework is compatible with prior approaches to zero-shot dense retrieval and consistently improves their performance. |
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels (2024.naacl-short)
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| Challenge: | Existing pointwise LLMs provide noisy or biased answers for documents that are partially relevant to the query. |
| Approach: | They propose to incorporate fine-grained relevance labels into the LLM prompt . they propose to better differentiate between documents with different levels of relevance . |
| Outcome: | The proposed model can differentiate between documents with different levels of relevance to the query and derive a more accurate ranking. |
GENRA: Enhancing Zero-shot Retrieval with Rank Aggregation (2024.emnlp-main)
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| 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. |
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)
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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. |
ELIOT: Zero-Shot Video-Text Retrieval through Relevance-Boosted Captioning and Structural Information Extraction (2025.naacl-srw)
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| Challenge: | Recent advances in video-text retrieval (VTR) have relied on supervised learning and fine-tuning. |
| Approach: | They propose a zero-shot video-text retrieval framework that leverages off-the-shelf captioners, large language models, and text retrieval methods without additional training or annotated data. |
| Outcome: | The proposed framework outperforms existing methods on video-text retrieval benchmarks without data. |
Scalable Zero-shot Entity Linking with Dense Entity Retrieval (2020.emnlp-main)
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| Challenge: | Existing methods for entity linking use manually curated mention tables and incoming Wikipedia link popularity. |
| Approach: | They propose a BERT-based entity linking model with a bi-encoder that embeds the mention context and the entity descriptions and then re-ranked the candidate with . they also evaluate the accuracy-speed trade-off inherent to large pre-trained models. |
| Outcome: | The proposed model is state-of-the-art on recent zero-shot benchmarks and established non-zero-shot evaluations. |
DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation (2025.naacl-long)
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| Challenge: | RAG systems leave out important relevant information (low recall) and excessively related but irrelevant information (high precision) authors propose a manual annotation-free schema that can be used for RAGs with limited performance. |
| Approach: | They propose a manual annotation-free schema that annotates unseen queries with calibrated relevance scores. |
| Outcome: | Evaluators show that DIRAS can achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs. |