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Baseline 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.

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