Home Community Meet Surv_ai: An Open Source Framework for Modeling and Comparative Evaluation Using AI Agents

Meet Surv_ai: An Open Source Framework for Modeling and Comparative Evaluation Using AI Agents

Meet Surv_ai: An Open Source Framework for Modeling and Comparative Evaluation Using AI Agents

We’ve used generative models resembling ChatGPT at the least once. In case you still haven’t, you need to. It’s imperative to know what we’re going to discuss today. While the outcomes provided by these large language models appear to be appropriate and admittedly a lot better, real, and natural than expected, there are still just a few things we miss out which will be quite essential if we’re using these models as a source of ground truth or a reference someplace else. 

The above paragraph entails an obvious query: what’s fallacious when it doesn’t appear to be at fault?

Nothing specifically is fallacious, but there are just a few questions that we’d like to ask while using these models, or at the least the outcomes produced by them someplace else, resembling

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  1. What’s the source of ground truth for these models? Where do they source their information from? It has to come back from somewhere.
  2. What in regards to the bias? Are these models biased? And, if that’s the case, can we estimate this bias? Can we counter it?
  3. What are the alternatives to the Model you might be using, and what if those perform higher in certain fact-checking scenarios? 

These are the precise issues that Daniel Balsam and the team have tackled with their project surv_ai. 

Surv_ai is a multi-agent large-language model framework designed for mult-agent modeling. This framework enables large-language models for use as engines to reinforce the standard of research, bias estimation, researching hypothesis, and doing the comparative evaluation in a a lot better and more efficient way, all packed under one hood. 

To completely understand what it does, it is necessary to know the core philosophy of this approach. The framework was inspired by a standard predictive analytics technique called bagging (bootstrap aggregating), an example of classic ensemble techniques. The thought revolves across the proven fact that as a substitute of 1 weak learner with an unlimited amount of knowledge, sometimes lots of weak performers with limited information, when aggregated, perform a lot better and provides higher-quality net results.

Similarly, multi-agent modeling involves generating multiple statistical models based on the actions of various agents. Within the case of Surv_ai, these models are made by agents querying and processing text from an information corpus. These agents then test and reason the hypothesis, in easy terms, whatever you might have asked them to confirm or give an opinion on and generate an appropriate response.

Because of the stochastic nature of huge language models, individual data points can vary. It may be countered by increasing the variety of agents employed.

Surv_ai employs two approaches a user can go for based on the necessities. One segment within the provided repository can produce multi-agent data points and is known as Survey. A Survey takes an announcement as input and returns the share of agents who agree. 

A more complex implementation is known as a Model, which might do what Survey can but with more control and nuances. You’ll be able to control lots of variation within the input parameters of a Model implementation and hence can increase the precision of results you would like to see. 

These implementations help us compare the bottom truth by the consolidated opinions of various agents. It may help us reach and analyze a hypothesis’s change of sentiment over time. It may also allow us to know and estimate the bias within the source information and bias within the large-language Model itself. 

Rapid advancements in large language models and generative engines are guaranteed to occur. Such a multi-agent modeling framework proves itself a promising and invaluable framework for such use cases. As claimed, it may possibly also function a possible and indispensable tool for researchers to analyze complex issues with quite a few aspects it relies on. It can be interesting to see how this evolves and adapts over time. 

Take a look at the Project. Don’t forget to hitch our 22k+ ML SubRedditDiscord Channel, and Email Newsletter, where we share the newest AI research news, cool AI projects, and more. If you might have any questions regarding the above article or if we missed anything, be at liberty to email us at Asif@marktechpost.com

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Anant is a Computer science engineer currently working as an information scientist with experience in Finance and AI products as a service. He’s keen to construct AI-powered solutions that create higher data points and solve every day life problems in an impactful and efficient way.

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