This article is a series of LLM articles, focusing on the translation of "Challenges and Applications of Large Language Models".
Challenges and applications of large language models
Summary
Within a few years, large language models (LLMs) went from non-existent to ubiquitous in machine learning discourse. Due to the rapid development of the field, it is difficult to identify remaining challenges and already fruitful application areas. In this paper, we aim to establish a systematic set of open problems and application successes so that ML researchers can more quickly understand the current state of the field and become productive.
1 Introduction
2 challenges
3 applications
3.1 Chatbot
3.2 Computational Biology
3.3 Computer program
3.4 Creative work
3.5 Knowledge work
3.6 Legal
3.7 Medicine
3.8 Reasoning
3.9 Robots and Embedded Agents
3.10 Social Sciences and Psychology
3.11 Synthetic data generation
4 related work
5 Conclusion
In this work, we identify several unresolved challenges for large language models, outline their current applications, and discuss how the former constrains the latter. By highlighting the limitations of existing methods, we hope to stimulate future research on these issues. We also hope that, by outlining methods used in different application areas, we can stimulate the exchange of ideas between fields and target further research.