Self Critique Pipeline

Self-critique pipelines represent a novel approach to improving the performance of large language models (LLMs) by incorporating a feedback loop where the model evaluates and refines its own output. Current research focuses on applying this technique to diverse tasks, including code translation, mathematical problem-solving, and video-to-text generation, often leveraging transformer-based architectures and employing methods like rejective fine-tuning and direct preference optimization. These pipelines aim to enhance LLM capabilities beyond simply increasing model size, leading to more robust and accurate performance across various domains, and potentially streamlining the development of complex AI systems.