Journal Article
Large Language Models

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Jingfeng Yang(Amazon (United States)), Hongye Jin(Texas A&M University), Ruixiang Tang(Rice University), Xiaotian Han(Texas A&M University), Qizhang Feng(Texas A&M University), Haoming Jiang(Amazon (United States)), Shaochen Zhong(Rice University), Bing Yin(Amazon (United States)), Xia Hu(Rice University)
February 28, 2024ACM Transactions on Knowledge Discovery from Data644 citations
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644

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0

Influential Citations

ACM Transactions on Knowledge Discovery from Data

Venue

2024

Year

Abstract

This article presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream Natural Language Processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. First, we offer an introduction and brief summary of current language models. Then, we discuss the influence of pre-training data, training data, and test data. Most importantly, we provide a detailed discussion about the use and non-use cases of large language models for various natural language processing tasks, such as knowledge-intensive tasks, traditional natural language understanding tasks, generation tasks, emergent abilities, and considerations for specific tasks. We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios. We also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice. This comprehensive guide aims to provide researchers and practitioners with valuable insights and best practices for working with LLMs, thereby enabling the successful implementation of these models in a wide range of NLP tasks. A curated list of practical guide resources of LLMs, regularly updated, can be found at https://github.com/Mooler0410/LLMsPracticalGuide . An LLMs evolutionary tree, editable yet regularly updated, can be found at llmtree.ai .

Analysis

Why This Paper Matters

Large Language Models (LLMs) like ChatGPT have rapidly transformed natural language processing, but practitioners often lack clear guidance on when and how to effectively deploy them. This survey by Yang et al. fills that gap by providing a comprehensive, practical guide that goes beyond theoretical discussions to address real-world usage. With 644 citations, it has become a key reference for those navigating the complexities of LLM adoption.

The paper's significance lies in its structured approach to dissecting LLM usage from three critical angles: models, data, and downstream tasks. By explicitly discussing both use and non-use cases, it helps practitioners avoid common pitfalls, such as applying LLMs to tasks where they underperform or ignoring data-related biases. This balanced perspective is essential for making informed decisions in production settings.

Moreover, the authors emphasize often-overlooked considerations like efficiency, cost, and latency, which are crucial for deployment. By bridging the gap between research and practice, this survey empowers organizations to harness LLMs effectively while mitigating risks.

Technical Contributions

  • Comprehensive Model Overview: Summarizes current language models, providing a taxonomy and evolutionary context.
  • Data-Centric Analysis: Examines the influence of pre-training, training, and test data on LLM behavior, highlighting data quality and bias issues.
  • Task-Specific Guidance: Offers detailed discussions on use and non-use cases across knowledge-intensive tasks, traditional NLU, generation, and emergent abilities.
  • Bias and Deployment Considerations: Addresses spurious biases and practical factors like efficiency, cost, and latency.
  • Curated Resources: Maintains a GitHub repository and an editable LLM evolutionary tree for ongoing reference.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes existing knowledge and provides qualitative insights. It illustrates applications and limitations through concrete use cases and non-use cases, but does not report quantitative metrics or comparisons. The value lies in its practical recommendations and frameworks rather than empirical findings.

Significance

This paper has significant implications for both practitioners and researchers. For practitioners, it serves as a handbook for deploying LLMs responsibly and efficiently, covering everything from task selection to cost management. For researchers, it identifies gaps and challenges, such as data biases and emergent abilities, that warrant further investigation. By fostering a deeper understanding of LLM capabilities and limitations, it contributes to the responsible and effective adoption of LLMs across industries. The regularly updated resources ensure the guide remains relevant in a fast-moving field.

large-language-modelsnlppractical-guidesurveychatgptdata-biasdeployment
Large Language Models

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