What is the impact of varying token misalignment thresholds in TAE on downstream task performance (e.g., MMLU,

Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous downstream applications. Nevertheless, akin to a double-edged sword, LLMs also present potential risks. They could suffer from private data leaks or yield inappropriate, harmful, or misleading content. Additionally, the rapid progress of LLMs raises concerns about the potential emergence of superintelligent systems without adequate safeguards. To effectively capitalize on LLM capacities as well as ensure their safe and

Research goal: What is the impact of varying token misalignment thresholds in TAE on downstream task performance (e.g., MMLU, HellaSwag) when applied to both Baichuan 2 and Vicuna-13B models?

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