DynRank: Improve Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification (2025.coling-main)
Copied to clipboard
Abdelrahman Elsayed Mahmoud Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed M.Abdelgwad, Adam Jatowt
| Challenge: | Existing approaches to enhancing passage retrieval rely on static prompts and pre-defined templates. |
| Approach: | They propose a dynamic question classification framework for open-domain question-answering systems that generates contextually relevant prompts. |
| Outcome: | The proposed framework improves passage retrieval in open-domain questionanswering systems by generating contextually relevant prompts. |
Similar Papers
Improving Passage Retrieval with Zero-Shot Question Generation (2022.emnlp-main)
Copied to clipboard
Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer
| Challenge: | Existing re-ranking methods for open-domain question answering are not domain- or task-specific. |
| Approach: | They propose a simple and effective re-ranking method for improving passage retrieval in open-domain question answering. |
| Outcome: | The proposed method outperforms strong supervised models on open-domain questions and triviaQA datasets on top-1000 passages. |
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent advances in neural retrieval have led to advancements on document, passage and knowledge-base benchmarks. |
| Approach: | They propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. |
| Outcome: | The proposed approach can exceed term-based techniques on document retrieval benchmarks by using domain-targeted synthetic question generation. |
Few-shot Reranking for Multi-hop QA via Language Model Prompting (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for multi-hop QA with open-domain questions require a large number of labeled question-document pairs for retrieval. |
| Approach: | They propose a language-based prompt for multi-hop path reranking that relies on language model prompting to generate a relevance score between a question and the path. |
| Outcome: | The proposed method yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples. |
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. |
| Approach: | They propose to use few-shot learning settings to fine-tune the sentiment classification model using manual or automatically generated prompts. |
| Outcome: | The proposed method outperforms the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task. |
Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that adding a instruction tuning stage to training large language models can improve zero-shot task generalization. |
| Approach: | They propose a method that retrieves promptspecific source prompt embeddings from training instances . they train soft prompt embeds for each prompt through prompt tuning and store the samples . |
| Outcome: | The proposed method outperforms hard prompts on unseen tasks by 2.39% points and outperformed 10 out of 11 datasets. |
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies show that training examples improve zero-shot performance of supervised ranking models. |
| Approach: | They propose to augment supervised ranking models with pairs of queries and documents to improve their performance. |
| Outcome: | The proposed model outperforms the unsupervised models on in-domain and out-domain retrieval benchmarks. |
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA (2024.naacl-long)
Copied to clipboard
| Challenge: | Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. |
| Approach: | They propose a framework to explicitly utilize the massive knowledge encoded in LLM parameters and their strong instruction understanding abilities. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on three widely-used ODQA datasets and achieves comparable performance with customized fine-tuned models on full training data. |
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Prior research focused on optimizing a single prompt as a continuous prompt, but this approach leads to a semantic space collapse, preventing differentiation between relevant and irrelevant passages. |
| Approach: | They propose a dense passage retrieval model that uses topic-based prompts and propose 'positive and negative sampling strategies' to boost dense retrieval efficiency. |
| Outcome: | The proposed model surpasses state-of-the-art retrieval techniques and improves space uniformity. |
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for event extraction require expensive annotation and are not extensible to new event ontologies. |
| Approach: | They propose to use textual entailment and/or question answering queries to extract a zero-shot event from a set of TE and/ or QA queries. |
| Outcome: | The proposed method achieves acceptable results on ACE-2005 and ERE, but there is still a large gap from supervised approaches. |
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text classification frameworks require large amounts of human-labeled documents to train . |
| Approach: | They propose a contrastive learning framework that improves zero-shot text classification . they add prompts to enhance label retrieval and use retrieved labels to enrich training . |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark text classification datasets. |