Extending the context window of large language models (LLMs) is getting popular recently, while the solution of augmenting LLMs with retrieval has existed for years. The natural questions are: i) Retrieval-augmentation versus long context window, which one is better for downstream tasks? ii) Can both methods be combined to get the best of both worlds? In this work, we answer these questions by studying both solutions using two state-of-the-art pretrained LLMs, i.e., a proprietary 43B GPT and LLaMA2-70B. Perhaps surprisingly, we find that LLM with 4K context window using simple retrieval-augmentation at generation can achieve comparable performance to finetuned LLM with 16K context window via positional interpolation on long context tasks, while taking much less computation. More importantly, we demonstrate that retrieval can significantly improve the performance of LLMs regardless of their extended context window sizes. Our best model, retrieval-augmented LLaMA2-70B with 32K context window, outperforms GPT-3.5-turbo-16k and Davinci003 in terms of average score on seven long context tasks including question answering and query-based summarization. It also outperforms its non-retrieval LLaMA2-70B-32k baseline by a margin, while being much faster at generation. Our study provides general insights on the choice of retrieval-augmentation versus long context extension of LLM for practitioners. https://arxiv.org/abs/2310.03025
sla RAG（検索による情報補完）と入出力長の拡大どちらがLLMにとって有効か、Q&Aと要約のタスクで検証したNVIDIA論文。 ・RAGは入力長の長短に関わらず性能を向上させる ・4k+RAGなら16kとほぼ同じ精度でより高速に推論できる ・RAG有りLLaMA2-70B-32kはGPT-3.5-turbo-16kとDavinci003を凌駕しベスト性能
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_philschmid RAG or longer context windows - what performs better? A new study from
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@nvidia
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compares retrieval augmentation generation (RAG) with increasing context window for large language models (LLMs) on long context question answering and summarization tasks.
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_philschmid Started from two base LLMs; NVIDIA created 43B GPT and Llama 2 70B.
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Extended context window to 16k for GPT-43B and 16K/32k for Llama using positional interpolation using the PILE dataset
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Instruction-tuned small and big context models on QA and summarization
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_philschmid Used 3 different Retriever (Dragon, Contriever, OpenAI embeddings) to embed and retrieve relevant context chunks.
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Evaluated on 7 datasets
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Compare the performance of LLM + Retrieval for 4k & 16k/32k against non-retrieval with full context. Also, compare to GPT-3.5-turbo
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_philschmid Open source Embedding Models/Retriever outperform OpenAI models
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The chunk size for embedding was 300 words
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The best performance of RAG was for 5-10 chunks
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Simple RAG + 4k LLM can match long context LLM.
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RAG + 32k LLM performs better than providing the full context
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_philschmid RAG Llama 2 70B outperforms GPT-3.5-turbo-16k (non-RAG).
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Positional interpolation is a simple method to extend context length by ~4-8x
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Check out the full paper https://arxiv.org/abs/2310.03025