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
SiLLM: Large Language Models for Simultaneous Machine Translation
Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang, Yang Feng
Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
Branislav Pecher, Ivan Srba, Maria Bielikova