Breakthroughs in LLMs have shifted NLP from task-specific methods to a generalized, data-driven approach, revolutionizing research and applications. Modern LLMs are increasingly being integrated with external tools, such as search engines, APIs, or symbolic reasoning systems to tackle complex tasks requiring specialized knowledge. However, their rise in usage has highlighted challenges in fairness, controllability, transparency, and explainability, which are especially critical qualities in domains like HR, legal, finance, and healthcare.
At Megagon Labs, we strive to harness the potential of LLMs while addressing these limitations. Our research focuses on three key areas:
- Understanding LLM Behavior and Limitations: Investigating how LLMs perform and the challenges they face in real-world production use cases.
- Advancing LLM Capabilities: Developing novel systems, hybrid neuro-symbolic approaches, and domain-specific innovations to enhance LLM performance.
- Robust Evaluation Methods: Creating effective methods to assess LLMs on complex, real-world tasks, ensuring their reliability and effectiveness in diverse applications.
By leveraging these techniques, we aim to improve the quality, consistency, fairness, and truthfulness of AI solutions tailored for HR and related domains, driving impactful progress in both research and practical applications. Our work encompasses fundamental research, applied projects, and open-source contributions, ensuring that our innovations make a meaningful impact both within and beyond the lab.
