ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities

• URL: http://arxiv.org/abs/2407.14482v2
• Date: Mon, 9 Sep 2024 06:19:07 GMT
• Title: ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities
• Authors: Peng Xu, Wei Ping, Xianchao Wu, Chejian Xu, Zihan Liu, Mohammad Shoeybi, Bryan Catanzaro,
• Abstract summary: ChatQA 2 is a Llama 3.0-based model with a 128K context window. We present a training recipe to extend the context window of Llama3-70B-base from 8K to 128K tokens. Our results demonstrate that the Llama3-ChatQA-2-70B model outperforms most existing state-of-the-art models.
• Score: 53.97515452727115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we introduce ChatQA 2, an Llama 3.0-based model with a 128K context window, designed to bridge the gap between open-source LLMs and leading proprietary models (e.g., GPT-4-Turbo) in long-context understanding and retrieval-augmented generation (RAG) capabilities. These two capabilities are essential for LLMs to process large volumes of information that cannot fit into a single prompt and are complementary to each other, depending on the downstream tasks and computational budgets. We present a detailed continued training recipe to extend the context window of Llama3-70B-base from 8K to 128K tokens, along with a three-stage instruction tuning process to enhance the model's instruction-following, RAG performance, and long-context understanding capabilities. Our results demonstrate that the Llama3-ChatQA-2-70B model outperforms most existing state-of-the-art models, including GPT-4-Turbo-2024-04-09, Qwen2-72B-Instruct, and Llama3.1-70B-Instruct, on ultra-long tasks beyond 100K tokens, as well as on the RAG benchmark using only a 4K context window, showing the strong long context capability across varying sequence lengths. We further provide extensive comparisons between direct long-context and RAG solutions using the same state-of-the-art long-context LLMs. Interestingly, we find that the performance of strong long-context LLMs using RAG improves when retrieving a larger number of chunks. With a large set of top-k chunks, RAG consistently outperforms direct long-context solution using the same state-of-the-art long-context models (e.g., Llama3-ChatQA-2-70B and Qwen2-72B-Instruct) on both 32K benchmarks and real-world 128K tasks. To advance research in this field, we open-sourced the model weights, training data, and the evaluation setup for the for the community: https://chatqa2-project.github.io/

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