Papers with neural retrieval models
Exploring efficient zero-shot synthetic dataset generation for Information Retrieval (2024.findings-eacl)
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| Challenge: | Recent advances in large language models offer a new avenue of generating synthetic training data to train neural retrieval models for unlabelled data collections. |
| Approach: | They propose a method to generate high-quality synthetic datasets using a small language model and a filtering mechanism to ensure the quality of generated questions. |
| Outcome: | The proposed method outperforms unsupervised retrieval methods such as BM25 and pretrained monoT5. |
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation (2021.eacl-main)
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| 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. |
Answering Complex Open-domain Questions Through Iterative Query Generation (D19-1)
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| Challenge: | Currently, one-step retrieve-and-read question answering systems cannot answer such questions because they rarely contain retrievable clues about the missing entity. |
| Approach: | They propose a multi-step approach to retrieve relevant content with the question, then reading the paragraphs returned by the information retrieval component to arrive at the final answer. |
| Outcome: | The proposed model outperforms the best previously published model despite not using pretrained language models such as BERT. |
Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration (2024.findings-acl)
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Sunhao Dai, Weihao Liu, Yuqi Zhou, Liang Pang, Rongju Ruan, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) have led to an influx of AI-generated content on the internet, transforming corpus of Information Retrieval (IR) systems from human-written to a coexistence with LLM-generated contents. |
| Approach: | They propose a benchmark named Cocktail that compares IR models with LLMs to find relevant documents and passages from a corpus. |
| Outcome: | The proposed benchmark aims to evaluate IR models in the mixed-sourced data landscape of the LLM era. |
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (D18-1)
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| Challenge: | Existing neural IR models do not have a mechanism for treating expansion terms differently from the original query terms, making it difficult to combine them with existing PRF approaches. |
| Approach: | They propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. |
| Outcome: | Extensive experiments on two standard test collections confirm the effectiveness of the proposed framework in improving the performance of two state-of-the-art neural IR models. |
Noisy Self-Training with Synthetic Queries for Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing neural retrieval models require training on a sufficient number of human-labelled query-passage pairs to work well. |
| Approach: | They propose a noisy self-training framework with synthetic queries to improve retrieval methods. |
| Outcome: | The proposed method outperforms baselines on general-domain and out-of-domain retrieval benchmarks on low-resource settings and is data efficient and data efficient. |