LLMs have demonstrated impressive capabilities, but often struggle with not strongly defined requests and generating high-quality output.
This talk explores a novel approach to address these limitations by leveraging the principles of GANs in a multi-turn dialogue setting.
We propose a framework where two LLMs engage in a conversational exchange, acting as both "generator" and "discriminator," to iteratively refine responses and better understand the user's underlying needs.
This can be achieved in an automated manner or with human intervention, allowing for greater control and improved results.
We will deep dive into the design of such a system, discuss its potential benefits (e.g., improved accuracy, reduced ambiguity, enhanced creativity), and explore the challenges of implementing and evaluating this collaborative LLM approach.

Tired of LLMs giving below expectations results? We're making them talk!
By pairing two LLMs in a GAN-inspired dialogue, we refine outputs and truly discover the user needs.
It's like a brainstorming session for AI, leading to better, more accurate results.
Join us to see the future of collaborative LLMs!