One Line
Researchers compared and evaluated agents and change models, specifically in terms of agent convenience in managing calendars and building conversational agents, and discussed challenges and insights in web agents and skill generation projects.
Slides
Slide Presentation (12 slides)
Key Points
- Researchers presented findings on the comparison between agents and change models in an online event.
- Agents can be utilized in various tasks, such as managing calendars and building conversational agents.
- Chrome extension called Eval was created to evaluate the performance of agents, showing a success rate of 30% and an average completion time of 25 seconds.
- Alternative approaches to evaluating agents include tools like Jungle for dataset exploration and reinforcement learning for evaluating based on unit test outcomes.
- Challenges in evaluating web agents include debugging failures and dealing with the dynamic nature of the web.
Summaries
38 word summary
Researchers compared agents and change models, focusing on agent convenience in managing calendars and building conversational agents. They addressed evaluating agent performance and challenges in web agents and skill generation projects, providing insights into agent evaluation and utilization.
64 word summary
Researchers discussed the comparison between agents and change models in an online event. They highlighted the convenience of using agents to manage calendars and explored building conversational agents using foundation models. Evaluating agent performance was addressed, along with challenges in evaluating web agents and skill generation projects. The presentation provided insights into agent evaluation and utilization, highlighting challenges and potential solutions in the field.
202 word summary
In an online event, researchers discussed the comparison between agents and change models. They explored different aspects of agents and their potential uses. One team member highlighted the convenience and efficiency of using an agent to manage their calendar. Another member presented their work on building conversational agents using foundation models and external memory. The importance of having a taxonomy of agent capabilities to understand their functionalities was also discussed.
The team then shared their individual projects. One member created a Chrome extension called Eval to evaluate agent performance, finding a 30% success rate and an average completion time of 25 seconds. Alternative approaches to evaluating agents were explored, including a tool called Jungle for dataset exploration and exportation, reinforcement learning for evaluation, and self-supervised evaluation based on conversational performance.
Challenges in evaluating web agents were addressed, such as debugging failures and the dynamic nature of the web. Skill generation projects like Voyager were also mentioned.
During the Q&A session, questions were answered about affordability of agent executors, tool integration, and prompting strategies. The event concluded with a lighthearted debate favoring agents over change.
Overall, the presentation provided insights into agent evaluation and utilization, highlighting challenges and potential solutions in the field.
334 word summary
A team of researchers presented their findings on the comparison between agents and change models in an online event. They discussed different aspects of agents and how they can be utilized in various tasks. One member of the team mentioned using an agent to manage their calendar, highlighting the convenience and efficiency it brings. Another member shared their work on building conversational agents using foundation models, evaluation, and external memory. They also discussed the importance of having a taxonomy of agent capabilities to better understand their functionalities.
The team then moved on to discuss their individual projects. One member presented their work on creating a Chrome extension called Eval, which evaluates the performance of agents by analyzing their actions and identifying pain points. They used a dataset called Mind2Web to benchmark the success rate and completion time of agents. The results showed that agents had a success rate of only 30% and took an average of 25 seconds to complete a task.
The team also explored alternative approaches to evaluating agents. One member developed a tool called Jungle, which allows users to explore and export datasets for evaluation. Another member used reinforcement learning to evaluate agents based on unit test pass/fail outcomes. They also mentioned a self-supervised evaluation approach where agents are evaluated based on conversational performance.
During the presentation, the team discussed the challenges of evaluating web agents, including debugging failures and dealing with the dynamic nature of the web. They also touched on the topic of skill generation and mentioned projects like Voyager that aim to generate skills through self-supervised learning.
In the Q&A session, they answered questions about the affordability of specific agent executors, the integration of tools with agents, and the use of prompting strategies. They concluded the event with a light-hearted debate on whether change or agents were better, with the majority favoring agents.
Overall, the presentation provided valuable insights into the evaluation and utilization of agents in various tasks, highlighting the challenges and potential solutions in this field.
Raw indexed text (47,364 chars / 8,839 words)
Source: https://youtu.be/bYLHklxEd_k?feature=shared