Fine Grained Citation

Fine-grained citation research focuses on improving the accuracy and verifiability of large language model (LLM) outputs by generating precise, sentence-level or even subsentence-level citations, addressing the problem of "hallucinations" in LLM-generated text. Current research explores novel training frameworks and algorithms, such as those employing coarse-to-fine pipelines or interleaved reference-claim generation, to enhance the quality and precision of these citations. This work is crucial for increasing the trustworthiness and reliability of LLM-generated content, particularly in academic and professional contexts where verifiable information is paramount.