Semantic Similarity of Fine-Tuning Data and Speculative Decoding Degradation in Qwen2.5 Multitask Instruction-Following

Abstract

Abstract: Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, arc

Research Question

What is the correlation between the semantic similarity of fine-tuning data and the degradation of speculative decoding performance when transitioning from single-task to multitask instruction-following scenarios in Qwen2.5?

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