In-context learning (ICL) is the capacity of a model to modify its behavior at inference time without updating its weights, allowing it to tackle new problems. Neural network architectures, such as transformers, have demonstrated this capability. However, recent research has found that ICL in transformers is influenced by certain linguistic data characteristics. Training transformers without these characteristics results in in-weight learning (IWL) instead. Overtraining and larger models have been explored as potential solutions. The duration of ICL is also found to be temporary. More research is needed to understand and optimize ICL in transformer neural networks.
In-Context Learning (ICL) in Transformer Neural Networks
In-Context Learning (ICL) is the capacity of a model to modify its behavior based on new inputs without updating its weights. This capability allows the model to tackle problems that were not present during training. Neural network architectures, specifically designed for few-shot knowledge, were the first to demonstrate this ability.
ICL has been observed in transformer-based models, such as GPT-3. However, recent research has shown that ICL in transformers is influenced by linguistic data characteristics, such as burstiness and distribution.
Researchers have found that transformers resort to in-weight learning (IWL) when trained on data lacking burstiness. IWL involves using data stored in the model’s weights instead of freshly supplied in-context information. ICL and IWL seem to be at odds with each other, with ICL emerging more easily when training data is bursty.
To better understand ICL in transformers, controlled investigations using established data-generating distributions are essential. Additionally, research has focused on developing smaller transformer models that can provide equivalent performance, including emergent ICL. Overtraining, where compact models are trained on more data than required, has emerged as a preferred method.
Key Takeaways:
- ICL allows models to modify their behavior without updating weights based on new inputs.
- ICL has been observed in transformer-based models, but it is influenced by linguistic data characteristics.
- In-weight learning (IWL) is used when training data lacks burstiness.
- Controlled investigations and smaller transformer models can help understand and achieve emergent ICL.
- Overtraining is a preferred method for developing compact transformer models.
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