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On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective

arXiv 2410.04541 ↗First posted 6 Oct 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Recent works have successfully applied Large Language Models (LLMs) to function modeling tasks. However, the reasons behind this success remain unclear. In this work, we propose a new evaluation framework to comprehensively assess LLMs' function modeling abilities. By adopting a Bayesian perspective of function modeling, we discover that LLMs are relatively weak in understanding patterns in raw data, but excel at utilizing prior knowledge about the domain to develop a strong understanding of the underlying function. Our findings offer new insights about the strengths and limitations of LLMs in the context of function modeling.

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References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2024The Llama 3 Herd of Modelscited by 14,715
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
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  7. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  8. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
  9. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  10. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  11. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 488
  12. 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researcherscited by 363
6 more
  1. 2023Proving Test Set Contamination in Black Box Language Modelscited by 230
  2. 2023Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solvecited by 178
  3. 2024Are self-explanations from Large Language Models faithful?cited by 101
  4. 2023GPT4GEO: How a Language Model Sees the World's Geographycited by 68
  5. 2024LLM Processes: Numerical Predictive Distributions Conditioned on Natural Languagecited by 55
  6. 2024Verbalized Machine Learning: Revisiting Machine Learning with Language Modelscited by 17

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