Language Model Output

Language model output research centers on improving the accuracy, reliability, and controllability of text generated by large language models (LLMs). Current efforts focus on detecting and correcting factual errors, mitigating biases, enhancing control over generation through techniques like prompt tuning and constrained decoding, and improving the explainability and trustworthiness of LLM outputs. This work utilizes various architectures, including transformers, and employs methods such as fact-checking, self-debiasing, and retrieval augmentation to achieve these goals. The ultimate aim is to create more reliable and responsible LLMs with broader applicability across diverse fields, from healthcare to legal document generation.