A controlled formal language task reveals fine-tuning outperforms in-context learning on in-distribution generalization but equals it on out-of-distribution, with ICL showing greater sensitivity to model size and tokenization.
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Fine-tuning on annotated English and Japanese dialogues improves clustering of backchannels and fillers and makes generated utterances closer to human ones.
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Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
A controlled formal language task reveals fine-tuning outperforms in-context learning on in-distribution generalization but equals it on out-of-distribution, with ICL showing greater sensitivity to model size and tokenization.
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Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models
Fine-tuning on annotated English and Japanese dialogues improves clustering of backchannels and fillers and makes generated utterances closer to human ones.