- There are some minor issues with the papers, but still, no strong reasons to reject them:
- I found that the creation of the dataset is optional. The Kialo dataset, well-studied in the community, provides exactly what the authors need, pairs of short claims and their counters. It is even cleaner than the dataset the authors created since no automatic processes exist to construct it. Still, what has been created in this paper can be extra data to learn from.
- The related work, especially regarding counter-argument generation, was shortly laid out with little elaboration about how previous works addressed that task and the implications.
- In the abstract, the authors claim they trained Arg-Judge with human preference. However, looking at the details of the data that the model is trained on, it turns out that the data is automatically created and does not precisely reflect human preferences.
- The procedure of creating the seed instructions, expanding them, and mapping them to inputs needed to be clarified. Providing examples here would be very helpful.
- I found that the creation of the dataset is optional. The Kialo dataset, well-studied in the community, provides exactly what the authors need, pairs of short claims and their counters. It is even cleaner than the dataset the authors created since no automatic processes exist to construct it. Still, what has been created in this paper can be extra data to learn from.