ADePT fine-tunes LLMs by teaching each token to adapt uniquely, like giving words their own personality.
The paper introduces ADePT, a method that enhances LLM fine-tuning by using adaptive token embeddings and a shared neural network to improve task performance while keeping parameters minimal.
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https://arxiv.org/abs/2501.03291
Original Problem 🔍:
Parameter-efficient fine-tuning of LLMs faces challenges with position-based token embedding offsets and sub-optimal shared embedding offsets, limiting model adaptation capabilities.
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Solution in this Paper 🛠️:
→ ADePT combines a short soft prompt with a shallow token-shared feed-forward neural network.
→ The network learns unique embedding offsets for each token, enabling adaptive adjustments based on model input.
→ ADePT generates input-specific token embedding offsets instead of position-based offsets.
→ The solution maintains parameter efficiency while improving task adaptation through better token optimization.
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Key Insights from this Paper 💡:
→ Position-based token embedding offsets restrict model generalization
→ Shared embedding offsets across tokens lead to sub-optimal performance
→ Token-specific adaptive offsets improve model adaptation capabilities
→ Parameter efficiency can be maintained while adding adaptive features
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Results 📊:
→ Tested across 23 NLP tasks and 4 PLM scales
→ Outperforms leading PEFT methods with fewer parameters
→ Surpasses full fine-tuning baseline in specific scenarios
→ Maintains comparable inference speed to DePT
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