NLP Task
Natural Language Processing (NLP) research currently focuses on enhancing Large Language Models (LLMs) for a wider range of tasks, including improved long-context processing, reliable benchmark creation using synthetic data, and seamless integration of generation and retrieval capabilities. Active research areas involve developing efficient frameworks for handling extensive input sequences within memory constraints, evaluating the effectiveness of LLMs across diverse and challenging benchmarks (including those for specialized domains like finance and law), and mitigating issues like data contamination and hallucination. These advancements are crucial for improving the reliability and applicability of LLMs in various real-world applications, from legal tech to healthcare and beyond.
Papers
Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. Churpek, Majid Afshar
Ask Question First for Enhancing Lifelong Language Learning
Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou, Qingwei Zhao