UKPLab/arxiv2025-inherent-limits-plms
Code repository for the paper "The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of Instruction Tuning and In-Context Learning Capabilities"
This project helps researchers and machine learning practitioners understand the underlying capabilities of large language models (LLMs). It takes information about how an LLM was trained (e.g., instruction-tuned or not) and specific tasks, then provides insights into whether instruction tuning fundamentally changes an LLM's abilities or simply makes pre-existing knowledge more accessible. This is useful for those evaluating or designing LLM training strategies.
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Use this if you are an AI researcher or machine learning engineer trying to decipher the true impact of instruction tuning on LLM performance versus the inherent capabilities from pre-training.
Not ideal if you are an end-user simply looking to apply or fine-tune an LLM for a specific real-world application without delving into its foundational training mechanisms.
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Python
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Apache-2.0
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Last pushed
Jan 16, 2025
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