Discussion & Conclusion

Introduction to Large Language Models for Statisticians

Emi Tanaka

15th October 2024

Workshop materials

All materials will be hosted at
https://emitanaka.org/workshop-LLM-2024/.



These workshop materials © 2024 by Emi Tanaka is licensed under CC BY-NC-ND 4.0

Ethical considerations

  • Bias and fairness: LLM can perpetuate existing social biases if they are trained on biased data or by reinforcing stereotypes.
  • Transparency and interpretability: Difficult to understand how the LLM arrived at a particular answer or prediction.
  • Job replacement and worker displacement: LLMs can replace human workers
  • Data sharing and usage: Usage of sensitive and protected information in training.
  • Misinformation: Spread of false or misleading information, reinforcing echo chambers or amplifying malicious actors.
  • Accountability: Who should be held accountable when a LLM makes an error?
  • Long-term impact on society: As LLM get increasing integrated in society, how would it impact the society in the lon-term?







Let’s discuss



How do LLMs complement or hinder statistical thinking?



What role should LLMs play in decision-making processes and research?



How will LLMs impact the training and development of future statisticians and data scientists?



What are the use cases of LLM for you (if any)?

Resources

  • Alammar & Grootendorst (2024) Hands-On Large Language Models. O’Reilly Media, Inc.
  • Atkinson-Abutridy (2024) Large Language Models: Concepts, Techniques and Applications. CRC Press.

emitanaka.org/workshop-LLM-2024/