The field of natural language processing (NLP) has been significantly revolutionized by the introduction of large language models (LLMs). These models, known for their ability to understand and generate human-like text across various tasks without specific training, have however encountered challenges in real-world deployment due to their intensive computational resource demands. Consequently, researchers have been examining the effectiveness of smaller, more compact LLMs in tasks like meeting summarization where performance and resource management is vital.
Historically, meeting transcript text summarization relied heavily on large models, requiring substantial annotated datasets and massive computational power for training. These models boast remarkable results, but their deployment is constrained by the high operational costs. In light of this, a recent study investigated the potential of compact LLMs to serve as an economical alternative to the larger models. The study evaluated models like FLAN-T5, TinyLLaMA, and LiteLLaMA, who were fine-tuned on specific datasets, against larger LLMs that were tested in a zero-shot manner, i.e., not trained specifically for the given task.
The key finding of the research was that compact LLMs, especially FLAN-T5, displayed performance comparable to, or even superior to, larger LLMs in summarizing meetings. Compared to models with parameters ranging from 7B to over 70B, FLAN-T5 with its 780M parameters was found to be highly effective and efficient. This discovery indicates that compact LLMs could provide a cost-efficient solution for NLP applications, finding an optimum balance between performance and computational demand.
In conclusion, the research uncovered promising potential for compact LLMs in meeting summarization tasks. The performance of models like FLAN-T5 indicates that smaller LLMs might surpass their larger counterparts, offering an effective and feasible alternative. This development has major implications for implementing NLP technologies, emphasizing a future where efficiency coexists with performance in practical applications. As the field progresses, compact LLMs are expected to play a major role in bridging the gap between innovative research and practical application.
The research was conducted by Muhammad Athar Ganaie a consulting intern at MarktechPost and a proponent of efficient deep learning. He is currently undertaking an M.Sc. in Electrical Engineering, specializing in Software Engineering, and his thesis research focuses on improving efficiency in deep reinforcement learning.
