Summary of Athena: Safe Autonomous Agents with Verbal Contrastive Learning, by Tanmana Sadhu et al.
Athena: Safe Autonomous Agents with Verbal Contrastive Learning
by Tanmana Sadhu, Ali Pesaranghader, Yanan Chen, Dong Hoon Yi
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents the Athena framework for ensuring the safety and trustworthiness of large language models (LLMs) as autonomous agents. These agents can understand instructions, interact with environments, and execute complex tasks using various tools. As their capabilities expand, it becomes crucial to guarantee their safe operation. The proposed framework employs verbal contrastive learning, leveraging past safe and unsafe trajectories to guide the agent towards safety while completing a task. Additionally, a critiquing mechanism is introduced to prevent risky actions at every step. To evaluate the safety reasoning ability of LLM-based agents, the authors curate a set of 80 toolkits across 8 categories with 180 scenarios, serving as a benchmark for future research. Experimental results demonstrate that verbal contrastive learning and interaction-level critiquing significantly improve the safety rate in both closed- and open-source LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence agents are safe and trustworthy. These agents can understand instructions, interact with their environment, and do tasks on their own. As they get more powerful, it’s crucial to make sure they don’t cause harm. The researchers developed a new system called Athena that helps these agents stay safe while doing tasks. They used past examples of safe and unsafe actions to teach the agent what is safe and what isn’t. They also created a way for the agent to think about its actions before taking them, so it doesn’t do something risky. To test their idea, they made a set of scenarios that agents could follow to see how well they did. The results show that this system helps agents stay safer. |
