Instructor / Outlines - Guided Generation

[manisnesan]

topic: guided generation

Guided Generation - Deliver structured outputs from the unstructured data using LLM

https://jxnl.github.io/instructor/why/

https://jxnl.github.io/instructor/concepts/prompting/

[manisnesan]

Good LLM Validation is Just Good Validation

Given the provided context and search results, here are the key takeaways, contributions, and implications from the article "Good LLM Validation is Just Good Validation":

• Key Takeaways:

  • Emphasizes the importance of robust validation techniques for Large Language Models (LLMs), highlighting that effective LLM validation aligns with general principles of good data validation[1][2].
  • Introduces llm_validator, a novel tool for probabilistic validation in software 3.0, leveraging LLMs for prompt engineering and validation tasks[1].
  • Discusses the integration of LLMs with pydantic validators to enhance system reliability through better error messages and system interpretations[2].
  • Highlights the simplicity and effectiveness of using LLMs for structured output, aiming to facilitate easier integration with applications like chatbots and custom apps[3].

• Contributions:

  • Provides a practical approach to incorporating LLMs into validation processes, offering a bridge between traditional data validation methods and the emerging needs of LLM-powered applications[1][2].
  • Showcases the potential of LLMs to improve software validation through examples and documentation, making the process more accessible to developers[3].

• Implications:

  • Suggests a shift towards more dynamic and adaptable validation frameworks to keep pace with the rapid development of LLM technologies[1].
  • Encourages developers to rethink validation strategies in the context of LLM capabilities, potentially leading to more efficient and effective software development practices[2].
  • Highlights the importance of understanding both the content and behavior of LLMs in validation processes, moving beyond traditional validation approaches[3].

Related Articles:

1. "Adapting model validation in the age of AI" - Discusses the importance of evolving validation approaches in response to the challenges posed by LLMs and AI technologies[4].
2. "Embracing Large Language Models for Medical Applications: Opportunities and Challenges" - Explores the potential and challenges of applying LLMs in the medical field, including validation concerns[5].
3. "Validating Large Language Models" - Addresses the regulatory and validation challenges associated with LLMs, emphasizing the need for comprehensive frameworks to ensure their responsible use[8].

Sources [1] Good LLM Validation is Just Good Validation - Instructor https://jxnl.github.io/instructor/blog/2023/10/23/good-llm-validation-is-just-good-validation/ [2] Good LLM Validation is just good data validation! Learn how to use pydantic's validators and build systems. | Jason Liu posted on the topic | LinkedIn https://www.linkedin.com/posts/jxnlco_good-llm-validation-is-just-good-validation-activity-7124784484981329920-9xpP?trk=public_profile_share_view [3] Good LLM Validation Is Just Good Validation https://news.ycombinator.com/item?id=38040391 [4] Adapting model validation in the age of AI https://www.deloitte.com/gh/en/Industries/financial-services/perspectives/adapting-model-validation.html [5] Embracing Large Language Models for Medical Applications: Opportunities and Challenges https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10292051/ [6] Large Language Models as Optimizers https://openreview.net/forum?id=Bb4VGOWELI [7] [PDF] Evaluating the Decency and Consistency of Data Validation Tests Generated by LLMs - arXiv https://arxiv.org/pdf/2310.01402.pdf [8] Validating Large Language Models https://deepchecks.com/regulating-large-language-models/ [9] Large language models encode clinical knowledge - Nature https://www.nature.com/articles/s41586-023-06291-2 [10] Building Boba AI https://martinfowler.com/articles/building-boba.html [11] Why don't we use validation/test sets more in LLM fine-tuning? https://www.reddit.com/r/LocalLLaMA/comments/16yuydd/why_dont_we_use_validationtest_sets_more_in_llm/ [12] Friend or foe? Exploring the implications of large language models on the science system - AI & SOCIETY https://link.springer.com/article/10.1007/s00146-023-01791-1 [13] The Impact of Multimodal Large Language Models on Health Care’s Future https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654899/ [14] OWASP Top 10 for Large Language Model Applications Explained: A Practical Guide | Lakera – Protecting AI teams that disrupt the world. https://www.lakera.ai/blog/owasp-top-10-for-large-language-model-applications-guide [15] LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation https://crpl.cis.udel.edu/oaccvv/about/publications/_index.files/Developing_LLM_Driven_Testsuite_for_Compiler_Validation_FGCS_JOURNAL_Oct2023.pdf [16] Evaluating large language models for use in healthcare - ScienceDirect.com https://www.sciencedirect.com/science/article/pii/S2352914823001508 [17] Evaluating Large Language Models https://toloka.ai/blog/evaluating-llms/ [18] Guidance for researchers and peer-reviewers on the ethical use of Large Language Models (LLMs) in scientific research workflows - AI and Ethics https://link.springer.com/article/10.1007/s43681-023-00294-5 [19] A study of generative large language model for medical research and healthcare - npj Digital Medicine https://www.nature.com/articles/s41746-023-00958-w [20] Processing Data for Large Language Models - Weights & Biases https://wandb.ai/wandb_gen/llm-data-processing/reports/Processing-Data-for-Large-Language-Models--VmlldzozMDg4MTM2 [21] Application of Large Language Models (LLMs) in Software Engineering: Overblown Hype or Disruptive Change? https://insights.sei.cmu.edu/blog/application-of-large-language-models-llms-in-software-engineering-overblown-hype-or-disruptive-change/

By Perplexity at https://www.perplexity.ai/search/httpsjxnlgithub-Summarize-the-LHovzEbuRB2sg6kD87Ajzw

[manisnesan]

Format your prompts

• Summary of the Blog Post:

  • The post discusses the limitations of formatting strings in various libraries for creating prompts for language models.
  • It critiques the approach of some libraries that require prompts to be submitted as a single string block, arguing that this simplification loses the richness of prompting techniques.
  • The author prefers the method where prompts are an array of messages, as seen in OpenAI's approach, which allows for more flexibility and complexity in prompt creation.
  • The post is a response to another post titled "Fuck you show me the prompt" by Hamel, emphasizing the need for more straightforward and effective prompt formatting methods.

• Key Takeaways:

  • Simplifying prompt formatting by requiring a single string block can limit the effectiveness and richness of prompts.
  • Using an array of messages for prompts, as OpenAI does, offers more flexibility and allows for more complex prompting techniques.
  • The author advocates for libraries to adopt a more straightforward approach to prompt formatting, similar to OpenAI's method.

• Contributions and Implications:

  • The post contributes to the ongoing discussion about the best practices for formatting prompts for language models.
  • It highlights the potential drawbacks of oversimplifying prompt formatting and advocates for a method that preserves the richness and complexity of prompts.
  • The implications of this discussion are significant for developers of language model libraries and for users who create prompts for these models, as it could influence future library designs and prompt creation strategies.

• Suggested Related Articles:

  1. "Python String Formatting Best Practices" from Real Python, which discusses different approaches to string formatting in Python[5].
  2. "Formatting String Objects" from Apple's developer documentation, providing insights into string formatting in Objective-C and Swift[3].
  3. "An extremely fast Python package installer and resolver" on GitHub, which might offer insights into efficient coding practices, including string handling[6].

Sources [1] Format your own prompts¶ https://jxnl.github.io/blog/writing/2024/02/20/formatting-strings/ [2] ScraperSpider https://dbaman.com [3] Formatting String Objects https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/Strings/Articles/FormatStrings.html [4] 吃枣药丸 https://www.msfconsole.cn [5] Python String Formatting Best Practices – Real Python https://realpython.com/python-string-formatting/ [6] github-trending/ at main · johe123qwe/github-trending https://github.com/johe123qwe/github-trending?search=1 [7] String Format Specifiers https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/Strings/Articles/formatSpecifiers.html [8] GitHub - sampsonv/github-trending https://github.com/sampsonv/github-trending [9] Python String Formatting: Overview and Real Examples | Udacity https://www.udacity.com/blog/2021/03/python-string-formatting-overview-and-real-examples.html [10] A Guide to the Newer Python String Format Techniques – Real Python https://realpython.com/python-formatted-output/ [11] A Clarified String Formatting Cheatsheet - DZone https://dzone.com/articles/how-to-format-a-string-clarified [12] String Formatting in Python https://www.telerik.com/blogs/string-formatting-python [13] Guide To String Formatting In Python | Simplilearn https://www.simplilearn.com/tutorials/python-tutorial/string-formatting-in-python

By Perplexity at https://www.perplexity.ai/search/httpsjxnlgithub-Summarize-the-9IyZfKsxTMGfw6MJoPclOA

[manisnesan]

https://medium.com/@winglian/augmenting-classification-datasets-with-mistral-large-for-deeper-reasoning-99dea57bd1d4

[manisnesan]

From Guided Generation using Outlines

Code - Colab

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