검색 증강 대규모 언어 모델에서 검색기 불일치 현상 완화 및 그 원인 규명
검색 증강 대규모 언어 모델(RALM)에서 다양한 검색기 간의 성능 불일치 현상이 광범위하게 존재하며, 이는 주로 지식 출처의 근본적인 차이와 판독 모델의 예측 불가능한 성능 저하에서 기인한다. 앙상블 기법을 활용한 검색기 조합과 적응형 검색 전략을 통해 이러한 불일치 문제를 완화하고 RALM의 전반적인 성능을 향상시킬 수 있다.
検索拡張型大規模言語モデルにおけるリトリーバの不整合性の解明と軽減
検索拡張型大規模言語モデル (RALM) は、異なるリトリーバを用いると、個々の事例レベルでの性能にばらつきが生じるという問題があり、その原因は知識源の違いとリーダーモデルの予期せぬエラーパターンにある。
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models for Open-Domain Question Answering
Retrieval-Augmented Language Models (RALMs) exhibit inconsistent performance across different retrievers due to inherent differences in knowledge sources and unpredictable errors in the reader model, but using an ensemble of retrievers can mitigate these inconsistencies and improve overall performance.
透過查詢生成混合與知識過濾提升檢索增強型大型語言模型：BlendFilter
BlendFilter 透過混合查詢生成和知識過濾技術，有效提升了檢索增強型大型語言模型在處理複雜問題和減少檢索知識噪音方面的效能。
クエリ生成ブレンディングと知識フィルタリングによる検索拡張大規模言語モデルの高度化：BlendFilter
複雑な質問やノイズの多い検索結果に対処するため、クエリ生成ブレンディングと知識フィルタリングを統合した新しいフレームワーク「BlendFilter」を提案する。
Parenting: A Novel Framework for Enhancing Knowledge Selection in Retrieval-Augmented Language Models by Decoupling Adherence and Robustness Parameters
Parenting, a novel framework, enhances the knowledge selection process in Retrieval-Augmented Language Models (RALMs) by decoupling parameters related to adherence and robustness, leading to a more balanced and effective integration of external knowledge.
Enhancing the Robustness of Retrieval-Augmented Language Models with CHAIN-OF-NOTE (CON)
CHAIN-OF-NOTE (CON), a novel framework for Retrieval-Augmented Language Models (RALMs), enhances their robustness by generating sequential reading notes for retrieved documents, enabling better assessment of relevance and integration of external knowledge for more accurate and reliable responses.
Improving Retrieval-Augmented Language Models' Robustness to Irrelevant Context
Retrieval-augmented language models can be made more robust to irrelevant retrieved context through a combination of natural language inference-based filtering and fine-tuning on a mixture of relevant and irrelevant contexts.
Efficient Retrieval-Augmented Language Models with Binary Token Representations
Binary token representations can significantly improve the inference speed and reduce the storage footprint of retrieval-augmented language models while maintaining high task performance.
Enhancing Language Models with Retrieval-Augmented Generation (RAG) 2.0
Retrieval-Augmented Generation (RAG) is a technique to enhance language models by providing additional context, enabling them to generate more specific and informative responses.
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