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FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
Zhuocheng Zhang, Yang Feng, Min Zhang
Retrieval-Augmented Generation (RAG) plays a pivotal role in modern large language model applications, with numerous existing frameworks offering a wide range of functionalities to…
LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
Zhuocheng Zhang, Yang Feng, Min Zhang
Retrieval-Augmented Generation (RAG) is a crucial method for mitigating hallucinations in Large Language Models (LLMs) and integrating external knowledge into their responses. Exis…
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features
Mengyu Bu, Shuhao Gu, Yang Feng
The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the…
Addressing the Length Bias Problem in Document-Level Neural Machine Translation
Zhuocheng Zhang, Shuhao Gu, Min Zhang +1
Document-level neural machine translation (DNMT) has shown promising results by incorporating more context information. However, this approach also introduces a length bias problem…
Enhancing Neural Machine Translation with Semantic Units
Langlin Huang, Shuhao Gu, Zhuocheng Zhang +1
Conventional neural machine translation (NMT) models typically use subwords and words as the basic units for model input and comprehension. However, complete words and phrases comp…