Automated Statistical Model Discovery with Language Models at Stanford University

แนวคิดหลัก

The author introduces a method for automated statistical model discovery using language models, leveraging their domain knowledge and programming capabilities to propose and critique statistical models without the need for a domain-specific language or handcrafted search procedures.

บทคัดย่อ

The content discusses the use of language models for automated statistical model discovery. It highlights the challenges in model discovery, the proposed method using Box's Loop framework, evaluation in different modeling settings, data extraction methods, and results from experiments on Gaussian process kernel discovery, open-ended probabilistic model discovery, and improving classic models under constraints.
Key points include:

Introduction of a method for automated statistical model discovery using language models.
Leveraging large language models to propose and critique statistical models.
Evaluation in various probabilistic modeling settings.
Comparison against expert-designed programs across different datasets.
Exploration of LM's ability to improve classic models under natural language constraints.
The results show promising outcomes in identifying effective models that match or outperform expert-designed programs across different datasets and scenarios.

สถิติ

"Our method matches the performance of previous systems."
"LMs reliably identify programs on par with expert programs."
"LM variations outperform baselines in improving classic models."

คำพูด

"The promise of LM driven model discovery is highlighted."
"Our approach is connected to recent work on hypothesis search and inductive reasoning with LMs."
"Leveraging LMs for automated model discovery is enticing at a conceptual level."

ข้อมูลเชิงลึกที่สำคัญจาก

Automated Statistical Model Discovery with Language Models

by Michael Y. L... ที่ arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17879.pdf

สอบถามเพิ่มเติม

How can the use of language models impact traditional statistical modeling practices

The use of language models can significantly impact traditional statistical modeling practices by automating and streamlining the model discovery process. Language models, with their broad domain knowledge and programming capabilities, can propose probabilistic programs for datasets, fit these models using automated inference techniques, evaluate model criticism statistics, and provide natural language feedback to guide future proposals. This approach eliminates the need for defining a domain-specific language of models or designing handcrafted search procedures. By leveraging language models in automated statistical model discovery, researchers can efficiently search over a vast space of candidate models without requiring extensive human expertise in modeling.

What are potential limitations or biases introduced by relying on language models for automated model discovery

While language models offer significant advantages in automated model discovery, there are potential limitations and biases that researchers should be aware of. One limitation is the reliance on pre-existing data patterns present in the training data used to train the language model. This could lead to a bias towards certain types of solutions or approaches based on common patterns seen during training. Additionally, if the dataset provided to the language model is not representative or contains inherent biases, it may influence the proposed models and their performance.
Another limitation is related to interpretability; complex neural network-based approaches generated by language models may lack transparency compared to more traditional statistical methods like linear regression or decision trees. This lack of interpretability could make it challenging for domain experts to understand and trust the results produced by these advanced models.

How might incorporating natural language constraints influence the interpretability and flexibility of discovered statistical models

Incorporating natural language constraints into automated statistical model discovery can have a profound impact on both interpretability and flexibility of discovered statistical models. By providing specific constraints expressed in natural language (e.g., "this model should be interpretable"), researchers can guide the language model towards generating solutions that balance between being understandable by domain experts while also being flexible enough to capture complex relationships within the data.
These constraints help shape how algorithms explore different modeling approaches and encourage them to prioritize certain characteristics such as simplicity or adherence to known scientific principles when proposing new statistical models. As a result, incorporating natural language constraints ensures that discovered statistical models align with both expert expectations regarding interpretability while still allowing for innovative solutions that push boundaries in terms of flexibility and accuracy.