Large language models (LLMs) have exhibited impressive performance on various text and multimodal tasks. However, applications such as document and video understanding, in-context learning, and scaling at inference time necessitate the capacity to process and reason over extended sequences of tokens. The limited context window of current LLMs presents a notable obstacle in these areas, as they might overlook crucial information spread across lengthy documents. Consequently, models may miss important details when dealing with large documents or videos that exceed their fixed-context windows. This limitation underlines the need for models adept at handling ultra-long contexts efficiently while maintaining performance on standard tasks.
Current strategies for extending the context of long-context language models can be categorized into three types: exact attention methods, approximate attention methods, and methods that integrate additional components. Techniques such as Position Interpolation, NTK-aware, Dynamic NTK, YaRN, and CLEX improve attention mechanisms by redesigning position embeddings. Recent advancements include models like GPT-4o, Gemini, and Claude, which support extensive context windows encompassing hundreds of thousands of tokens, but their closed-source nature inhibits reproducibility. Open-source initiatives like ProLong employ NTK-aware scaling, which necessitates substantial computation, whereas Gradient uses continued pretraining to preserve standard task performance.
Researchers from UIUC and NVIDIA have developed an efficient training approach for creating ultra-long-context LLMs from aligned instruct models, extending context lengths from 128K to 1M, 2M, and 4M tokens. This methodology employs efficient, continued pretraining techniques to widen the context window while utilizing instruction tuning to retain instruction-following and reasoning capabilities. Moreover, their UltraLong-8B model sets a new benchmark across diverse long-context evaluations. Models trained using this method maintain strong performance on standard benchmarks, indicating balanced improvements for tasks involving both long and short contexts. The research offers a comprehensive analysis of key design choices, illustrating the effects of different scaling strategies and data compositions.
The proposed method is composed of two primary stages: continued pretraining and instruction tuning. These stages collectively facilitate the effective processing of ultra-long inputs while sustaining robust task performance. A YaRN-based scaling approach is implemented for extending context with fixed hyperparameters set to α = 1 and β = 4, as opposed to NTK-aware scaling strategies. The scale factors are calculated based on the target context length and apply larger scaling factors for RoPE embeddings to handle extended sequences and prevent performance drop at maximum lengths. Researchers draw from high-quality SFT datasets in general, mathematics, and code domains for training data and further leverage GPT-4o and GPT-4o-mini for refining responses and conducting thorough data cleaning.
The models introduced in this study demonstrate superior retrieval capabilities in the Needle in a Haystack passkey retrieval test. While baseline models like Llama-3-8B-Instruct-Gradient-1048k succeed, others like Llama3.1-8B-Instruct and Llama-3-8B-ProLong-512k-Instruct exhibit errors. In contrast, UltraLong models achieve 100% accuracy across all input lengths and depths, showcasing strong retrieval abilities. UltraLong scores the highest average on RULER for inputs up to 512K and 1M tokens, the highest F1 scores on LV-Eval within 128K and 256K token lengths, and the best performance on InfiniteBench. Additionally, these models maintain excellent performance across general, math, and code domains with average scores of 62.47, 61.06, and 60.95, surpassing the base model’s 61.45.
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