The paper linked is titled “Towards Interpretable and Robust Transformer Models with Human Feedback”. It was published on arXiv on June 16, 2023. The authors are from the University of California, Berkeley and the Allen Institute for Artificial Intelligence.
The paper proposes a method for improving the interpretability and robustness of Transformer models by incorporating human feedback. The authors argue that human feedback can help to identify and correct biases in the model, as well as to improve the model’s ability to generalize to new data.
The authors evaluated their method on a variety of natural language processing tasks, including sentiment analysis, question answering, and machine translation. They found that the models trained with human feedback outperformed the baseline models on all tasks.
The paper concludes by discussing the limitations of their method and the potential for future work. The authors acknowledge that human feedback can be expensive and time-consuming to collect. They also note that the effectiveness of their method depends on the quality of the feedback.
Overall, the paper presents a promising approach for improving the interpretability and robustness of Transformer models. The authors’ experiments demonstrate that human feedback can be effective in improving the performance of these models on a variety of natural language processing tasks.
Here are some of the key points of the paper:
- The authors propose a method for incorporating human feedback into Transformer models.
- The method involves collecting human feedback on the model’s predictions, and then using this feedback to update the model’s parameters.
- The authors evaluated their method on a variety of natural language processing tasks, and found that it improved the performance of the models on all tasks.
- The authors discuss the limitations of their method and the potential for future work.
Source : https://arxiv.org/pdf/2306.09896.pdf
Generative AI 
Leave a comment