Inversion learning generates model-specific NLG evaluation prompts from a single human-annotated sample, and these prompts outperform hand-crafted and search-based prompts in correlation with human scores.
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
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abstract
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as high computational demands and privacy concerns. This paper focuses on developing efficient Small Language Models (SLMs) and Multimodal Small Language Models (MSLMs) that retain competitive reasoning abilities. We introduce a novel training pipeline that enhances reasoning capabilities and facilitates deployment on edge devices, achieving state-of-the-art performance while minimizing development costs. \InfR~ aims to advance AI systems by improving reasoning, reducing adoption barriers, and addressing privacy concerns through smaller model sizes. Resources are available at https://github. com/Reallm-Labs/InfiR.
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cs.CL 1years
2025 1verdicts
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Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts
Inversion learning generates model-specific NLG evaluation prompts from a single human-annotated sample, and these prompts outperform hand-crafted and search-based prompts in correlation with human scores.