Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization? (AI summary)

Key Points

1. The research paper investigates whether smaller, compact Large Language Models (LLMs) can be a cost-efficient alternative to larger LLMs for meeting summarization in real-world industrial environments.

2. The study compares the performance of fine-tuned compact LLMs, such as FLAN-T5, TinyLLaMA, and LiteLLaMA, with zero-shot larger LLMs, including LLaMA-2, GPT-3.5, and PaLM-2, in meeting summarization tasks.

3. The experimental results indicate that most smaller LLMs, even after fine-tuning, fail to outperform larger zero-shot LLMs in meeting summarization datasets. However, FLAN-T5 stands out as a notable exception, performing on par with or even better than many zero-shot larger LLMs while being significantly smaller.

4. The paper also presents insights into the cost-effective utilization of LLMs for summarizing business meeting transcripts and evaluates the LLMs based on various metrics, including accuracy, inference cost, and computational resource requirements.

5. It discusses the limitations of deploying LLMs in the real world, particularly in terms of production costs, dataset availability for model training, and instruction-following capabilities of smaller language models.

6. The study includes an evaluation of the performance of LLMs based on different instructions for generating long, medium, and short summaries, as well as presenting insights on cost efficiency, inference speed, and human evaluation results.

Summary

The paper investigates the performance of smaller, compact language models (LLMs) compared to larger LLMs in the context of meeting summarization in an industrial environment. The study involves comparing fine-tuned compact LLMs, such as FLAN-T5, with zero-shot larger LLMs like GPT-3.5 and PaLM-2, and assessing the implications for real-world industrial deployment. The findings indicate that most smaller LLMs, even after fine-tuning, fail to outperform larger zero-shot LLMs in meeting summarization datasets. However, FLAN-T5, with 780M parameters, performs equally well or better than many zero-shot larger LLMs, making it a cost-efficient solution for real-world industrial deployment.

Limitations of LLM Deployment in Real-World Systems
The paper also addresses the limitations of LLM deployment in real-world systems, highlighting the substantial computing resources required and the difficulty in obtaining large annotated datasets for fine-tuning smaller language models. Furthermore, the study evaluates the instruction-following capabilities of smaller and larger LLMs in diverse scenarios and discusses the cost-effectiveness of utilizing LLMs for business meeting summarization.
Key contributions of the paper include an extensive evaluation of smaller LLMs compared to larger LLMs, investigations into different datasets for performance analysis, and the demonstration of advantages of deploying smaller LLMs for real-world usage. The study also explores the implications of various LLM models in terms of cost, inference speed, and computational resource requirements for deployment in real-world industrial settings.

Emphasizing Study Limitations 

The limitations of the study are emphasized, including the constraints of instruction variations, the use of GPT-4 generated summaries as reference instead of human annotations, and the focus on truncating meeting transcripts. The paper concludes by stating that future work should investigate the effects of dataset size and consider additional instructions for LLM evaluation. Additionally, the paper mentions licensing compliance, privacy protection, and human evaluation without the need for additional compensation.
<b>Conclusion and Future Work</b>
Overall, the study provides valuable insights into the performance and potential real-world deployment of smaller LLMs compared to larger LLMs in the context of meeting summarization tasks.

Reference: https://arxiv.org/abs/2402.008...