Non Negative Textual Response

Non-negative textual response research focuses on generating accurate, helpful, and unbiased text outputs from large language models (LLMs), addressing issues like hallucinations (fabricating information) and biases. Current research emphasizes improving the faithfulness of LLM responses to input context, often using retrieval-augmented generation (RAG) or fine-tuning techniques to enhance model accuracy and reduce reliance on inherent biases. This work is crucial for building trustworthy LLMs applicable to various fields, including healthcare, education, and customer service, where reliable and unbiased information is paramount.