Papers with HyDE
Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA (2026.eacl-srw)
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| Challenge: | Existing studies evaluate RAG methods in isolation and focus on single-turn settings. |
| Approach: | They compare retrieval-augmented generation methods for multi-turn conversational QA with those that use dialogue history and coreference to ground large language models. |
| Outcome: | The proposed methods outperform vanilla RAG and advanced methods fail to yield gains and can even degrade performance below the No-RAG baseline. |
Efficient Diagnosis Assignment Using Unstructured Clinical Notes (2023.acl-short)
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| Challenge: | Electronic phenotyping entails using electronic health records (EHRs) to identify patients with specific clinical outcomes and determine when those outcomes occurred. |
| Approach: | They propose a framework for electronic phenotyping that integrates labeling functions and a disease-agnostic neural network to assign diagnoses to patients. |
| Outcome: | The proposed framework disambiguates hypertension true positives and false positives with a supervised area under the precision-recall curve (AUPRC) of 0.85. |
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. |
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)
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| Challenge: | Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. |
| Approach: | They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite . |
| Outcome: | The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query . |