Log-likelihood vectors establish a consistent KL divergence scale across pretraining, model sizes, seeds, quantization, fine-tuning, and layers, revealing subdiffusive trajectories and early stabilization in Pythia models.
In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
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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.
LLM-generated image aesthetics labels correlate with Mercari user behavior and produced sales growth in an online experiment.
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Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings
Log-likelihood vectors establish a consistent KL divergence scale across pretraining, model sizes, seeds, quantization, fine-tuning, and layers, revealing subdiffusive trajectories and early stabilization in Pythia models.
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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.
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Image Score: Learning and Evaluating Human Preferences for Mercari Search
LLM-generated image aesthetics labels correlate with Mercari user behavior and produced sales growth in an online experiment.