Instruction tuning bridges base model gaps but stays bound by pretraining priors.
The paper investigates the correlation between the performance of instruction-tuned and base LLMs, concluding that instruction tuning does not introduce fundamentally new capabilities but extends base model performance based on pretraining priors and instruction-tuning data.
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Solution in this Paper: 👨🔧
→ The paper compares base and instruction-tuned models across tasks using LLaMA-2 and other LLM families.
→ It introduces methods like the SampleGen model for generating in-context examples, analyzing generalization and task-solving independently.
→ Results are benchmarked across tasks included and excluded from instruction-tuning datasets, revealing limitations tied to pretraining priors.
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https://arxiv.org/abs/2501.08716
Original Problem: 🤔:
→ LLMs struggle with some tasks solvable by children and their capabilities remain unpredictable across task complexities.
→ The impact of instruction tuning on LLMs' inherent limitations is poorly understood, leading to questions about its role in expanding model abilities.
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Key Insights: 💡
→ Instruction tuning enhances model understanding of prompts but does not remove base model limitations tied to pretraining data.
→ Performance correlation exists between instruction-tuned and base models across diverse tasks, even when confounding factors are controlled.
→ Instruction tuning improves generalization to new tasks but relies heavily on priors from pretraining data.
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Results:
→ Instruction-tuned models correlate significantly with base models, with Spearman's r = 0.851.
→ SampleGen-Pipeline demonstrates enhanced generalization but fails on tasks outside pretraining priors.
→ Out-of-distribution tasks see no improvement over base model performance.
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