Long Form Answer

Long-form question answering (LFQA) focuses on generating detailed, paragraph-level responses to complex questions, moving beyond short, factual answers. Current research emphasizes improving the accuracy, coherence, and factuality of these long-form responses, often employing retrieval-augmented generation (RAG) models and large language models (LLMs) while addressing challenges like hallucination and bias in both generation and evaluation. This field is crucial for advancing human-computer interaction and building more robust and reliable AI systems capable of nuanced understanding and explanation, with applications ranging from education and legal aid to customer service and scientific research. Significant effort is also dedicated to developing better evaluation metrics that accurately reflect human judgment of answer quality.