Adapting Sequence Models for Sentence Correction

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.Comment: EMNLP 201

Hybrid Retrieval-Augmented Generation for Real-time Composition Assistance

Retrieval augmented models show promise in enhancing traditional language models by improving their contextual understanding, integrating private data, and reducing hallucination. However, the processing time required for retrieval augmented large language models poses a challenge when applying them to tasks that require real-time responses, such as composition assistance. To overcome this limitation, we propose the Hybrid Retrieval-Augmented Generation (HybridRAG) framework that leverages a hybrid setting that combines both client and cloud models. HybridRAG incorporates retrieval-augmented memory generated asynchronously by a Large Language Model (LLM) in the cloud. By integrating this retrieval augmented memory, the client model acquires the capability to generate highly effective responses, benefiting from the LLM's capabilities. Furthermore, through asynchronous memory integration, the client model is capable of delivering real-time responses to user requests without the need to wait for memory synchronization from the cloud. Our experiments on Wikitext and Pile subsets show that HybridRAG achieves lower latency than a cloud-based retrieval-augmented LLM, while outperforming client-only models in utility