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From the Wilderness to the Lab: UltraIF Improves Instruction Following in Language Models

Large language models (LLMs) have made enormous progress in recent years and have become indispensable tools in many areas. However, their ability to follow instructions and handle complex tasks is still limited. There is a significant discrepancy between the performance of models trained by leading companies and those developed based on open-source data. A new method called UltraIF now promises to close this gap and significantly improve the instruction following capabilities of LLMs trained with publicly available data.

UltraIF pursues a novel approach based on the decomposition of complex instructions into simpler components. User requests are broken down into queries, constraints, and associated evaluation questions. A specially trained "UltraComposer" combines these elements into new, more complex instructions and simultaneously generates evaluation questions to check the quality of the responses. This process makes it possible to synthesize more demanding instructions while simultaneously filtering the model's responses based on the evaluation questions.

The developers of UltraIF achieved impressive results in their experiments. They demonstrated that it is possible to train an LLaMA-3.1-8B-Base model to match the performance of its Instruct counterpart in five different instruction-following benchmarks – without any information about the benchmarks themselves. Remarkably, an 8B model was used for both generating the responses and the evaluation. The optimized model also achieved competitive results in other benchmarks. Furthermore, the experiments showed that UltraIF can also further improve the performance of LLaMA-3.1-8B-Instruct through self-alignment, highlighting the potential of the method for diverse applications.

The core idea of UltraIF is to reduce the complexity of real-world user requests and extract the underlying structures. By breaking them down into smaller, more easily understood units, the model can better process the instructions and generate more precise responses. The use of evaluation questions enables automated quality control and helps to ensure the accuracy and relevance of the responses.

The results of the study suggest that UltraIF is a promising approach to improving instruction following in LLMs. The method is scalable and can be used with open-source data, making it accessible to a broad community of developers and researchers. The ability to further enhance the performance of existing models through self-alignment also opens up new perspectives for the development of more powerful and efficient language models.

The research findings on UltraIF are an important contribution to the further development of language models and could help to reduce the gap between open-source models and commercial solutions. The method offers a promising tool for the development of LLMs that can better understand complex instructions and generate more precise answers, ultimately leading to an improved user experience and new application possibilities.

Bibliography: - https://chatpaper.com/chatpaper/zh-CN?id=3&date=1738857600&page=1 - https://arxiv.org/abs/2311.07911 - https://arxiv.org/abs/2410.12877 - https://news.ycombinator.com/item?id=38544729 - https://openreview.net/pdf?id=ez6Cb0ZGzG - https://www.quora.com/Can-the-average-person-train-to-complete-an-ultra-marathon - https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf - https://www.quora.com/Should-you-upgrade-to-a-Samsung-S23-Ultra-if-you-have-an-S22-Ultra - https://forums.whirlpool.net.au/archive/30082nw3-6 - Hugging Face - Papers - arxiv:2502.04153 - UltraIF: Advancing Instruction Following from the Wild ```