From Thinking to Knowing: Using Natural Language Confidence From LLM Thought Processes
“And this is wisdom and temperance and self-knowledge — for a man to know what he knows, and what he does not know.” - Plato, Charmides “To say you know when you know, and to say you do not when you do not — that is knowledge.” - Confucius Special thanks to Yash Sharma for a lot of valuable feedback on my idea and evaluation methodology Research code: https://github.com/pranavc28/thought-engineering-and-self-awareness Claim Thought engineering techniques (overthinking and automated confidence refinement) improve multi-classification performance across all model architectures. The purpose of this blog is to explain these terms, and prove why this is true. ...
Temperature Sampling for OCR-VQA: Does It Matter?
Research code: https://github.com/pranavc28/temperature-ocr-vqa Definitions Temperature in LLMs controls how predictable or exploratory a model’s outputs are. Low temperature = consistent and factual, good for precise tasks. High temperature = more diverse, good for creative tasks—but also riskier. Visual Question Answering (VQA) is about answering questions directly from images. For OCR tasks, like reading a book cover, VQA can outperform raw OCR because it focuses only on what’s asked (e.g., “Who’s the author?”) instead of dumping every piece of text. ...