Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.06474 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909337996230656 |
|---|---|
| author | Pan, Jiayi Zhang, Yichi Tomlin, Nicholas Zhou, Yifei Levine, Sergey Suhr, Alane |
| author_facet | Pan, Jiayi Zhang, Yichi Tomlin, Nicholas Zhou, Yifei Levine, Sergey Suhr, Alane |
| contents | We show that domain-general automatic evaluators can significantly improve the performance of agents for web navigation and device control. We experiment with multiple evaluation models that trade off between inference cost, modularity of design, and accuracy. We validate the performance of these models in several popular benchmarks for digital agents, finding between 74.4 and 92.9% agreement with oracle evaluation metrics. Finally, we use these evaluators to improve the performance of existing agents via fine-tuning and inference-time guidance. Without any additional supervision, we improve state-of-the-art performance by 29% on the popular benchmark WebArena, and achieve around 75% relative improvement in device control settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autonomous Evaluation and Refinement of Digital Agents Pan, Jiayi Zhang, Yichi Tomlin, Nicholas Zhou, Yifei Levine, Sergey Suhr, Alane Artificial Intelligence We show that domain-general automatic evaluators can significantly improve the performance of agents for web navigation and device control. We experiment with multiple evaluation models that trade off between inference cost, modularity of design, and accuracy. We validate the performance of these models in several popular benchmarks for digital agents, finding between 74.4 and 92.9% agreement with oracle evaluation metrics. Finally, we use these evaluators to improve the performance of existing agents via fine-tuning and inference-time guidance. Without any additional supervision, we improve state-of-the-art performance by 29% on the popular benchmark WebArena, and achieve around 75% relative improvement in device control settings. |
| title | Autonomous Evaluation and Refinement of Digital Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2404.06474 |