Summary of Interacting Large Language Model Agents. Interpretable Models and Social Learning, by Adit Jain et al.
Interacting Large Language Model Agents. Interpretable Models and Social Learning
by Adit Jain, Vikram Krishnamurthy
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes novel methods for interacting large language model agents (LLMAs) by combining techniques from statistical signal processing and microeconomics. The authors aim to understand and mitigate the biases exhibited by these agents, which learn from prior decisions and external inputs. Specifically, they develop interpretable models and stochastic control algorithms that enable LLMAs to interact and perform Bayesian inference. The paper has three main results: (1) a utility maximization framework for individual LLMAs, (2) an interpretable model for sequential interactions between LLMAs and the environment, and (3) a stochastic control framework to delay herding behavior and improve state estimation accuracy in centralized or autonomous settings. The authors demonstrate their methods’ effectiveness on real datasets for hate speech classification and product quality assessment using open-source models like Mistral and closed-source models like ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine computer programs that can talk to each other and learn from what they say. This paper figures out how these “language model agents” work when they interact with each other and the world. They use special math and ideas from economics to understand why these agents sometimes make bad decisions or follow what others do. The authors create new ways for these agents to think more clearly and make better choices. They test their methods on real-world problems like detecting hate speech online. The main idea is that these computer programs act like smart, learning humans when they interact with each other. |
Keywords
» Artificial intelligence » Bayesian inference » Classification » Language model » Large language model » Signal processing
