Hyperparameters and Baseline Experiments in Dialog Systems
Too Long; Didn't ReadBaseline experiments in dialog systems unfold with key hyperparameter settings. Models trained for five epochs, extended to ten for erroneous dialogs, featured a batch size of 32, learning rate of 5e − 5, and AdamW optimizer. LLAMA adopted unique finetuning parameters. Results, reflected in Table 17, elucidate the interplay of data quality, system errors, and model performance through F1-Score and BLEU metrics.