From 7e06d8e6ba3a3dee94fce9e7276ffdd58e4ba3fb Mon Sep 17 00:00:00 2001 From: souzatharsis Date: Mon, 25 Nov 2024 20:37:47 -0300 Subject: [PATCH] update structured --- .../_build/.doctrees/environment.pickle | Bin 786629 -> 793709 bytes .../_build/.doctrees/notebooks/evals.doctree | Bin 82419 -> 82486 bytes .../notebooks/structured_output.doctree | Bin 97546 -> 100038 bytes tamingllms/_build/html/_images/rebuttal.png | Bin 0 -> 70249 bytes .../notebooks/structured_output.ipynb | 13 +++ .../_static/structured_output/rebuttal.png | Bin 0 -> 70249 bytes tamingllms/_build/html/notebooks/evals.html | 64 +++++------ .../html/notebooks/structured_output.html | 101 ++++++++++-------- tamingllms/_build/html/objects.inv | Bin 670 -> 711 bytes tamingllms/_build/html/searchindex.js | 2 +- .../jupyter_execute/markdown/intro.ipynb | 2 +- .../notebooks/structured_output.ipynb | 13 +++ .../_static/structured_output/rebuttal.png | Bin 0 -> 70249 bytes tamingllms/notebooks/structured_output.ipynb | 13 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b/tamingllms/_build/html/_sources/notebooks/structured_output.ipynb @@ -777,6 +777,19 @@ "\n", "On the other hand, those findings are not without criticism. The .txt team challenges the findings of the paper \"Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models\" {cite}`tam2024letspeakfreelystudy`. The paper claims that structured output formats, like JSON, negatively affect the performance of LLMs, particularly when it comes to reasoning. The rebuttal argues that **structured generation, when done correctly, actually *improves* performance**.\n", "\n", + "\n", + "```{figure} ../_static/structured_output/rebuttal.png\n", + "---\n", + "name: structured_vs_unstructured\n", + "alt: Structured vs Unstructured Results by .txt team\n", + "scale: 60%\n", + "align: center\n", + "---\n", + "Structured vs Unstructured Results by .txt team.\n", + "```\n", + "The .txt team presents compelling evidence through their reproduction of the paper's experiments. While their unstructured results align with the original paper's findings, their structured results paint a dramatically different picture - demonstrating that structured generation actually improves performance (see {numref}`structured_vs_unstructured`). The team has made their experimental notebooks publicly available on GitHub for independent verification {cite}`dottxt2024demos`.\n", + "\n", + "\n", ".txt team identifies several flaws in the methodology of \"Let Me Speak Freely?\" that they believe led to inaccurate conclusions:\n", "\n", "* The paper finds that structured output improves performance on classification tasks but doesn't reconcile this finding with its overall negative conclusion about structured output. \n", diff --git a/tamingllms/_build/html/_static/structured_output/rebuttal.png b/tamingllms/_build/html/_static/structured_output/rebuttal.png new file mode 100644 index 0000000000000000000000000000000000000000..d1a3659f6ba54aadf77bf03b47edc50ad764170a GIT binary patch literal 70249 zcmbTeby$>Z7dMKcvQ>mFC+;$gWrk<&=f2natzSGrkCkPvT%x>$hlh7XPF7MC5AS>@9v=R? ze=fk^BoH|Y!+(UFr8J$@?980qj2unzl#HD1ZS0(FERAounmRgJ+S&533$b&v-m-9Z zws#Wd;6VKM2iWZ#%{gu>;}5}`T(pBzSHp2Zm^Gzbd67LKio}A=^r|yZ%qi*ik 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-

4. Challenges of Evaluating LLM-based Applications

+

4. Challenges of Evaluating LLM-based Applications

Evals are surprisingly often all you need.

—Greg Brockman, OpenAI’s President

@@ -197,38 +197,38 @@

Contents

-

4.1. Non-Deterministic Machines

+

4.1. Non-Deterministic Machines

One of the most fundamental challenges when building products with Large Language Models (LLMs) is their non-deterministic nature. Unlike traditional software systems where the same input reliably produces the same output, LLMs can generate different responses each time they’re queried - even with identical prompts and input data. This characteristic is both a strength and a significant engineering challenge.

When you ask ChatGPT or any other LLM the same question multiple times, you’ll likely get different responses. This isn’t a bug - it’s a fundamental feature of how these models work. The “temperature” parameter, which controls the randomness of outputs, allows models to be creative and generate diverse responses. However, this same feature makes it incredibly difficult to build reliable, testable systems.

Consider a financial services company using LLMs to generate investment advice. The non-deterministic nature of these models means that:

@@ -239,7 +239,7 @@

-

4.1.1. Temperature and Sampling

+

4.1.1. Temperature and Sampling

The primary source of non-determinism in LLMs comes from their sampling strategies. During text generation, the model:

  1. Calculates probability distributions for each next token

  2. @@ -248,7 +248,7 @@

    -

    4.1.2. The Temperature Spectrum

    +

    4.1.2. The Temperature Spectrum

    • Temperature = 0: Most deterministic, but potentially repetitive

    • Temperature = 1: Balanced creativity and coherence

    • @@ -347,7 +347,7 @@

      -

      4.2. Emerging Properties

      +

      4.2. Emerging Properties

      Beyond their non-deterministic nature, LLMs present another fascinating challenge: emergent abilities that spontaneously arise as models scale up in size. These abilities - from basic question answering to complex reasoning - aren’t explicitly programmed but rather emerge “naturally” as the models grow larger and are trained on more data. This makes evaluation fundamentally different from traditional software testing, where capabilities are explicitly coded and can be tested against clear specifications.

      Emerging Properties @@ -359,7 +359,7 @@

      -

      4.3. Problem Statement

      +

      4.3. Problem Statement

      Consider a practical example that illustrates these challenges: building a customer support chatbot powered by an LLM. In traditional software development, you would define specific features (like handling refund requests or tracking orders) and write tests to verify each function. But with LLMs, you’re not just testing predefined features - you’re trying to evaluate emergent capabilities like understanding context, maintaining conversation coherence, and generating appropriate emotional responses.

      This fundamental difference raises critical questions about evaluation:

-

4.6. Tools

+

4.6. Tools

-

4.7. References

+

4.7. References

[WTB+22] diff --git a/tamingllms/_build/html/notebooks/structured_output.html b/tamingllms/_build/html/notebooks/structured_output.html index b4fcf32..d0f4677 100644 --- a/tamingllms/_build/html/notebooks/structured_output.html +++ b/tamingllms/_build/html/notebooks/structured_output.html @@ -192,7 +192,7 @@
-

3. Wrestling with Structured Output

+

3. Wrestling with Structured Output

In limits, there is freedom. Creativity thrives within structure.

—Julia B. Cameron

@@ -200,44 +200,44 @@

Contents

-

3.1. The Structured Output Challenges

+

3.1. The Structured Output Challenges

Large language models (LLMs) excel at generating human-like text, but they often struggle to produce output in a structured format consistently. This poses a significant challenge when we need LLMs to generate data that can be easily processed by other systems, such as databases, APIs, or other software applications.

Sometimes, even with a well-crafted prompt, an LLM might produce an unstructured response when a structured one is expected. This can be particularly challenging when integrating LLMs into systems that require specific data formats.

Throughout this notebook, we will consider as input a segment of a sample SEC filing of Apple Inc.

@@ -343,7 +343,7 @@

-

3.2. Problem Statement

+

3.2. Problem Statement

Obtaining structured output from LLMs presents several significant challenges:

  • Inconsistency: LLMs often produce unpredictable results, sometimes generating well-structured output and other times deviating from the expected format.

  • @@ -352,10 +352,10 @@

    -

    3.3. Solutions

    +

    3.3. Solutions

    Several strategies and tools can be employed to address the challenges of structured output from LLMs.

    -

    3.3.1. Strategies

    +

    3.3.1. Strategies

    • Schema Guidance: Providing the LLM with a clear schema or blueprint of the desired output structure helps to constrain its generation and improve consistency. This can be achieved by using tools like Pydantic to define the expected data structure and then using that definition to guide the LLM’s output.

    • Output Parsing: When LLMs don’t natively support structured output, parsing their text output using techniques like regular expressions or dedicated parsing libraries can extract the desired information. For example, you can use regular expressions to extract specific patterns from the LLM’s output, or you can use libraries like Pydantic to parse the output into structured data objects.

    • @@ -363,9 +363,9 @@

      -

      3.3.2. Techniques and Tools

      +

      3.3.2. Techniques and Tools

      -

      3.3.2.1. One-Shot Prompts

      +

      3.3.2.1. One-Shot Prompts

      In one-shot prompting, you provide a single example of the desired output format within the prompt.

      @@ -432,7 +432,7 @@

      -

      3.3.2.2. Structured Output with Provider-Specific APIs

      +

      3.3.2.2. Structured Output with Provider-Specific APIs

      One-shot prompting is a simple technique that can lead to material improvements in structured output, though may not be sufficient for complex (e.g. nested) structures and / or when the model’s output needs to be restricted to a specific set of options or types.

      Provider-specific APIs can offer ways to handle those challenges. We will explore two approaches here using OpenAI’s API:

        @@ -441,7 +441,7 @@

        -

        3.3.2.3. JSON Mode

        +

        3.3.2.3. JSON Mode

        JSON mode is a feature provided by some LLM APIs, such as OpenAI’s, that allows the model to generate output in JSON format. This is particularly useful when you need structured data as a result, such as when parsing the output programmatically or integrating it with other systems that require JSON input. As depicted in Fig. 3.1, JSON mode is implemented by instructing theLLM model to use JSON as response format and optionally defining a target schema.

        JSON Mode @@ -579,7 +579,7 @@

        -

        3.3.3. LangChain

        +

        3.3.3. LangChain

        LangChain is a framework designed to simplify the development of LLM applications. It provider an abstraction layer over many LLM providers, including OpenAI, that offers several tools for parsing structured output.

        In particular, LangChain offers the with_structured_output method, which can be used with LLMs that support structured output APIs, allowing you to enforce a schema directly within the prompt.

        @@ -639,8 +639,8 @@

        .with_structured_output() can be found here.

      -

      3.3.4. Outlines

      -

      Outlines [Out24]is a library specifically focused on structured text generation from LLMs. Under the hood, Outlines works by adjusting the probability distribution of the model’s output logits - the raw scores from the final layer of the neural network that are normally converted into text tokens. By introducing carefully crafted logit biases, Outlines can guide the model to prefer certain tokens over others, effectively constraining its outputs to a predefined set of valid options. This provides fine-grained control over the model’s generation process. In that way, Outlines provides several powerful features:

      +

      3.3.4. Outlines

      +

      Outlines [Out24]is a library specifically focused on structured text generation from LLMs. Under the hood, Outlines works by adjusting the probability distribution of the model’s output logits - the raw scores from the final layer of the neural network that are normally converted into text tokens. By introducing carefully crafted logit biases, Outlines can guide the model to prefer certain tokens over others, effectively constraining its outputs to a predefined set of valid options. This provides fine-grained control over the model’s generation process. In that way, Outlines provides several powerful features:

      • Multiple Choice Generation: Restrict the LLM output to a predefined set of options.

      • Regex-based structured generation: Guide the generation process using regular expressions.

      • @@ -663,7 +663,7 @@

        -

        3.3.4.1. A Simple Example: Multiple Choice Generation

        +

        3.3.4.1. A Simple Example: Multiple Choice Generation

        TOP = 100
        @@ -687,7 +687,7 @@ 

        -

        3.3.4.2. Pydantic model

        +

        3.3.4.2. Pydantic model

        Outlines allows to guide the generation process so the output is guaranteed to follow a JSON schema or Pydantic model. Now we will go back to our example of extracting entities and places from a SEC filing. In order to do so, we simply need to pass our Pydantic model to the json method in Outlines’ generate module.

        @@ -717,9 +717,9 @@

        -

        3.4. Discussion

        +

        3.4. Discussion

        -

        3.4.1. Comparing Solutions

        +

        3.4.1. Comparing Solutions

        • Simplicity vs. Control: One-shot prompts are simple but offer limited control. LangChain, and Outlines provide greater control but might have a steeper learning curve though quite manageable.

        • Native LLM Support: with_structured_output in LangChain relies on the underlying LLM having built-in support for structured output APIs, i.e. LangChain is just a wrapper around the LLM’s structured output API. Other methods, like parsing or using Outlines, are more broadly applicable with the fomer enabling a wider range of Open Source models.

        • @@ -728,7 +728,7 @@

          -

          3.4.2. Best Practices

          +

          3.4.2. Best Practices

          • Clear Schema Definition: Define the desired output structure clearly. This can be done in several ways including schemas, types, or Pydantic models as appropriate. This ensures the LLM knows exactly what format is expected.

          • Descriptive Naming: Use meaningful names for fields and elements in your schema. This makes the output more understandable and easier to work with.

          • @@ -737,15 +737,22 @@

            -

            3.4.3. Ongoing Debate on Structured Output from LLMs

            +

            3.4.3. Ongoing Debate on Structured Output from LLMs

            The use of structured output, like JSON or XML, for Large Language Models (LLMs) is a developing area. While structured output offers clear benefits in parsing, robustness, and integration, there is growing debate on whether it also potentially comes at the cost of reasoning abilities.

            -

            Recent research [TWT+24] suggests that imposing format restrictions on LLMs might impact their performance, particularly in reasoning-intensive tasks. Further evidence [Aid24] suggests LLMs may produce lower quality code if they’re asked to return it as part of a structured JSON response:

            +

            Recent research [TWT+24] suggests that imposing format restrictions on LLMs might impact their performance, particularly in reasoning-intensive tasks. Further evidence [Aid24] suggests LLMs may produce lower quality code if they’re asked to return it as part of a structured JSON response:

            • Potential performance degradation: Enforcing structured output, especially through constrained decoding methods like JSON-mode, can negatively impact an LLM’s reasoning abilities. This is particularly evident in tasks that require multi-step reasoning or complex thought processes.

            • Overly restrictive schemas: Imposing strict schemas can limit the expressiveness of LLM outputs and may hinder their ability to generate creative or nuanced responses. In certain cases, the strictness of the schema might outweigh the benefits of structured output.

            • Increased complexity in prompt engineering: Crafting prompts that effectively guide LLMs to generate structured outputs while maintaining performance can be challenging. It often requires careful consideration of the schema, the task instructions, and the desired level of detail in the response.

            -

            On the other hand, those findings are not without criticism. The .txt team challenges the findings of the paper “Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models” [TWT+24]. The paper claims that structured output formats, like JSON, negatively affect the performance of LLMs, particularly when it comes to reasoning. The rebuttal argues that structured generation, when done correctly, actually improves performance.

            +

            On the other hand, those findings are not without criticism. The .txt team challenges the findings of the paper “Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models” [TWT+24]. The paper claims that structured output formats, like JSON, negatively affect the performance of LLMs, particularly when it comes to reasoning. The rebuttal argues that structured generation, when done correctly, actually improves performance.

            +
            +Structured vs Unstructured Results by .txt team +
            +

            Fig. 3.2 Structured vs Unstructured Results by .txt team.

            +
            +
            +

            The .txt team presents compelling evidence through their reproduction of the paper’s experiments. While their unstructured results align with the original paper’s findings, their structured results paint a dramatically different picture - demonstrating that structured generation actually improves performance (see Fig. 3.2). The team has made their experimental notebooks publicly available on GitHub for independent verification [Dot24].

            .txt team identifies several flaws in the methodology of “Let Me Speak Freely?” that they believe led to inaccurate conclusions:

            • The paper finds that structured output improves performance on classification tasks but doesn’t reconcile this finding with its overall negative conclusion about structured output.

            • @@ -759,21 +766,25 @@

              -

              3.5. Conclusion

              +

              3.5. Conclusion

              Extracting structured output from LLMs is crucial for integrating them into real-world applications. By understanding the challenges and employing appropriate strategies and tools, developers can improve the reliability and usability of LLM-powered systems, unlocking their potential to automate complex tasks and generate valuable insights.

        -

        3.6. References

        -
        -
        +

        3.6. References

        +
        +
        [Aid24]

        Aider. Code in json: structured output for llms. https://aider.chat/2024/08/14/code-in-json.html, 2024. Accessed: 2024.

        -
        +
        +[Dot24] +

        Dottxt. Say what you mean: demos. https://github.com/dottxt-ai/demos/tree/main/say-what-you-mean, 2024. Accessed: 2024.

        +
        +
        [Out24]

        Outlines. Type-safe structured output from llms. https://dottxt-ai.github.io/outlines/latest/, 2024. Accessed: 2024.

        -
        +
        [TWT+24] (1,2)

        Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, and Yun-Nung Chen. Let me speak freely? a study on the impact of format restrictions on performance of large language models. 2024. URL: https://arxiv.org/abs/2408.02442, arXiv:2408.02442.

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Python Environment Setup": [[0, "python-environment-setup"]], "2. API Keys Configuration": [[0, "api-keys-configuration"]], "3. Code Repository": [[0, "code-repository"]], "Troubleshooting Common Issues": [[0, "troubleshooting-common-issues"]], "Taming Large Language Models": [[1, "taming-large-language-models"]], "Chapter 1: Introduction": [[1, "chapter-1-introduction"]], "Chapter 2: Non-determinism & Evals": [[1, "chapter-2-non-determinism-evals"]], "Chapter 3: Wrestling with Structured Output": [[1, "chapter-3-wrestling-with-structured-output"]], "Chapter 4: Hallucination: The Reality Gap": [[1, "chapter-4-hallucination-the-reality-gap"]], "Chapter 5: The Cost Factor": [[1, "chapter-5-the-cost-factor"]], "Chapter 6: Safety Concerns": [[1, "chapter-6-safety-concerns"]], "Chapter 7: Size and Length Limitations": [[1, "chapter-7-size-and-length-limitations"]], "Chapter 8: Breaking Free from Cloud Providers": [[1, "chapter-8-breaking-free-from-cloud-providers"]], "Appendix A: Code Examples": [[1, "appendix-a-code-examples"]], "Appendix B: Tools and Resources": [[1, "appendix-b-tools-and-resources"]], "Challenges of Evaluating LLM-based Applications": [[2, "challenges-of-evaluating-llm-based-applications"]], "Non-Deterministic Machines": [[2, "non-deterministic-machines"]], "Temperature and Sampling": [[2, "temperature-and-sampling"]], "The Temperature Spectrum": [[2, "the-temperature-spectrum"]], "Emerging Properties": [[2, "emerging-properties"]], "Problem Statement": [[2, "problem-statement"], [3, "problem-statement"], [4, "problem-statement"]], "Evals of Traditional Software vs LLMs": [[2, "evals-table"]], "Evals Design": [[2, "evals-design"]], "Conceptual Overview": [[2, "conceptual-overview"]], "Design Considerations": [[2, "design-considerations"]], "Key Components": [[2, "key-components"]], "Examples: The Dataset": [[2, "examples-the-dataset"]], "Metrics: The Metrics Layer": [[2, "metrics-the-metrics-layer"]], "Evaluator: The Assessment Layer": [[2, "evaluator-the-assessment-layer"]], "Leaderboard: The Ranking Layer": [[2, "leaderboard-the-ranking-layer"]], "Tools": [[2, "tools"]], "References": [[2, "references"], [3, "references"], [4, "references"]], "Output Size Limitations": [[3, "output-size-limitations"]], "What are Token Limits?": [[3, "what-are-token-limits"]], "Token Cost and Length Limitation Comparison Across Key Models": [[3, "token-cost-table"]], "Content Chunking with Contextual Linking": [[3, "content-chunking-with-contextual-linking"]], "Generating long-form content": [[3, "generating-long-form-content"]], "Step 1: Chunking the Content": [[3, "step-1-chunking-the-content"]], "Example Usage": [[3, "example-usage"]], "Discussion": [[3, "discussion"], [4, "discussion"]], "Implications": [[3, "implications"]], "Future Considerations": [[3, "future-considerations"]], "Conclusion": [[3, "conclusion"], [4, "conclusion"]], "Wrestling with Structured Output": [[4, "wrestling-with-structured-output"]], "The Structured Output Challenges": [[4, "the-structured-output-challenges"]], "Solutions": [[4, "solutions"]], "Strategies": [[4, "strategies"]], "Techniques and Tools": [[4, "techniques-and-tools"]], "One-Shot Prompts": [[4, "one-shot-prompts"]], "Structured Output with Provider-Specific APIs": [[4, "structured-output-with-provider-specific-apis"]], "JSON Mode": [[4, "json-mode"]], "LangChain": [[4, "langchain"]], "Outlines": [[4, "outlines"]], "A Simple Example: Multiple Choice Generation": [[4, "a-simple-example-multiple-choice-generation"]], "Pydantic model": [[4, "pydantic-model"]], "Comparing Solutions": [[4, "comparing-solutions"]], "Best Practices": [[4, "best-practices"]], "Ongoing Debate on Structured Output from LLMs": [[4, "ongoing-debate-on-structured-output-from-llms"]]}, "indexentries": {}}) \ No newline at end of file +Search.setIndex({"docnames": ["markdown/intro", "markdown/toc", "notebooks/evals", "notebooks/output_size_limit", "notebooks/structured_output"], "filenames": ["markdown/intro.md", "markdown/toc.md", "notebooks/evals.ipynb", "notebooks/output_size_limit.ipynb", "notebooks/structured_output.ipynb"], "titles": ["1. 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"length": [1, 3], "limit": [1, 3], "8": 1, "break": 1, "free": 1, "from": [1, 4], "cloud": 1, "provid": [1, 4], "appendix": 1, "exampl": [1, 2, 3, 4], "b": 1, "tool": [1, 2, 4], "resourc": 1, "evalu": 2, "llm": [2, 4], "base": 2, "applic": 2, "determinist": 2, "machin": 2, "temperatur": 2, "sampl": 2, "spectrum": 2, "emerg": 2, "properti": 2, "problem": [2, 3, 4], "statement": [2, 3, 4], "tradit": 2, "softwar": 2, "v": 2, "design": 2, "conceptu": 2, "overview": 2, "consider": [2, 3], "compon": 2, "dataset": 2, "metric": 2, "layer": 2, "assess": 2, "leaderboard": 2, "rank": 2, "refer": [2, 3, 4], "what": 3, "ar": 3, "token": 3, "comparison": 3, "across": 3, "chunk": 3, "contextu": 3, "link": 3, "gener": [3, 4], "long": 3, "form": 3, "step": 3, "usag": 3, "discuss": [3, 4], "implic": 3, "futur": 3, "conclus": [3, 4], "solut": 4, "strategi": 4, "techniqu": 4, "One": 4, "shot": 4, "prompt": 4, "specif": 4, "json": 4, "mode": 4, "langchain": 4, "outlin": 4, "simpl": 4, "multipl": 4, "choic": 4, "pydant": 4, "compar": 4, "best": 4, "ongo": 4, "debat": 4}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinxcontrib.bibtex": 9, "sphinx": 57}, "alltitles": {"Introduction": [[0, "introduction"]], "Contents": [[0, "contents"], [2, "contents"], [3, "contents"], [4, "contents"]], "Core Challenges We\u2019ll Address": [[0, "core-challenges-we-ll-address"]], "A Practical Approach": [[0, "a-practical-approach"]], "A Note on Perspective": [[0, "a-note-on-perspective"]], "Who This Book Is For": [[0, "who-this-book-is-for"]], "Outcomes": [[0, "outcomes"]], "Prerequisites": [[0, "prerequisites"]], "Setting Up Your Environment": [[0, "setting-up-your-environment"]], "1. Python Environment Setup": [[0, "python-environment-setup"]], "2. API Keys Configuration": [[0, "api-keys-configuration"]], "3. Code Repository": [[0, "code-repository"]], "Troubleshooting Common Issues": [[0, "troubleshooting-common-issues"]], "Taming Large Language Models": [[1, "taming-large-language-models"]], "Chapter 1: Introduction": [[1, "chapter-1-introduction"]], "Chapter 2: Non-determinism & Evals": [[1, "chapter-2-non-determinism-evals"]], "Chapter 3: Wrestling with Structured Output": [[1, "chapter-3-wrestling-with-structured-output"]], "Chapter 4: Hallucination: The Reality Gap": [[1, "chapter-4-hallucination-the-reality-gap"]], "Chapter 5: The Cost Factor": [[1, "chapter-5-the-cost-factor"]], "Chapter 6: Safety Concerns": [[1, "chapter-6-safety-concerns"]], "Chapter 7: Size and Length Limitations": [[1, "chapter-7-size-and-length-limitations"]], "Chapter 8: Breaking Free from Cloud Providers": [[1, "chapter-8-breaking-free-from-cloud-providers"]], "Appendix A: Code Examples": [[1, "appendix-a-code-examples"]], "Appendix B: Tools and Resources": [[1, "appendix-b-tools-and-resources"]], "Challenges of Evaluating LLM-based Applications": [[2, "challenges-of-evaluating-llm-based-applications"]], "Non-Deterministic Machines": [[2, "non-deterministic-machines"]], "Temperature and Sampling": [[2, "temperature-and-sampling"]], "The Temperature Spectrum": [[2, "the-temperature-spectrum"]], "Emerging Properties": [[2, "emerging-properties"]], "Problem Statement": [[2, "problem-statement"], [3, "problem-statement"], [4, "problem-statement"]], "Evals of Traditional Software vs LLMs": [[2, "evals-table"]], "Evals Design": [[2, "evals-design"]], "Conceptual Overview": [[2, "conceptual-overview"]], "Design Considerations": [[2, "design-considerations"]], "Key Components": [[2, "key-components"]], "Examples: The Dataset": [[2, "examples-the-dataset"]], "Metrics: The Metrics Layer": [[2, "metrics-the-metrics-layer"]], "Evaluator: The Assessment Layer": [[2, "evaluator-the-assessment-layer"]], "Leaderboard: The Ranking Layer": [[2, "leaderboard-the-ranking-layer"]], "Tools": [[2, "tools"]], "References": [[2, "references"], [3, "references"], [4, "references"]], "Output Size Limitations": [[3, "output-size-limitations"]], "What are Token Limits?": [[3, "what-are-token-limits"]], "Token Cost and Length Limitation Comparison Across Key Models": [[3, "token-cost-table"]], "Content Chunking with Contextual Linking": [[3, "content-chunking-with-contextual-linking"]], "Generating long-form content": [[3, "generating-long-form-content"]], "Step 1: Chunking the Content": [[3, "step-1-chunking-the-content"]], "Example Usage": [[3, "example-usage"]], "Discussion": [[3, "discussion"], [4, "discussion"]], "Implications": [[3, "implications"]], "Future Considerations": [[3, "future-considerations"]], "Conclusion": [[3, "conclusion"], [4, "conclusion"]], "Wrestling with Structured Output": [[4, "wrestling-with-structured-output"]], "The Structured Output Challenges": [[4, "the-structured-output-challenges"]], "Solutions": [[4, "solutions"]], "Strategies": [[4, "strategies"]], "Techniques and Tools": [[4, "techniques-and-tools"]], "One-Shot Prompts": [[4, "one-shot-prompts"]], "Structured Output with Provider-Specific APIs": [[4, "structured-output-with-provider-specific-apis"]], "JSON Mode": [[4, "json-mode"]], "LangChain": [[4, "langchain"]], "Outlines": [[4, "outlines"]], "A Simple Example: Multiple Choice Generation": [[4, "a-simple-example-multiple-choice-generation"]], "Pydantic model": [[4, "pydantic-model"]], "Comparing Solutions": [[4, "comparing-solutions"]], "Best Practices": [[4, "best-practices"]], "Ongoing Debate on Structured Output from LLMs": [[4, "ongoing-debate-on-structured-output-from-llms"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/tamingllms/_build/jupyter_execute/markdown/intro.ipynb b/tamingllms/_build/jupyter_execute/markdown/intro.ipynb index 8122d38..a02a4c1 100644 --- a/tamingllms/_build/jupyter_execute/markdown/intro.ipynb +++ b/tamingllms/_build/jupyter_execute/markdown/intro.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "04e29306", + "id": "af54935e", "metadata": {}, "source": [ "(intro)=\n", diff --git a/tamingllms/_build/jupyter_execute/notebooks/structured_output.ipynb b/tamingllms/_build/jupyter_execute/notebooks/structured_output.ipynb index a2dab3d..77c3ff4 100644 --- a/tamingllms/_build/jupyter_execute/notebooks/structured_output.ipynb +++ b/tamingllms/_build/jupyter_execute/notebooks/structured_output.ipynb @@ -777,6 +777,19 @@ "\n", "On the other hand, those findings are not without criticism. The .txt team challenges the findings of the paper \"Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models\" {cite}`tam2024letspeakfreelystudy`. The paper claims that structured output formats, like JSON, negatively affect the performance of LLMs, particularly when it comes to reasoning. The rebuttal argues that **structured generation, when done correctly, actually *improves* performance**.\n", "\n", + "\n", + "```{figure} ../_static/structured_output/rebuttal.png\n", + "---\n", + "name: structured_vs_unstructured\n", + "alt: Structured vs Unstructured Results by .txt team\n", + "scale: 60%\n", + "align: center\n", + "---\n", + "Structured vs Unstructured Results by .txt team.\n", + "```\n", + "The .txt team presents compelling evidence through their reproduction of the paper's experiments. While their unstructured results align with the original paper's findings, their structured results paint a dramatically different picture - demonstrating that structured generation actually improves performance (see {numref}`structured_vs_unstructured`). The team has made their experimental notebooks publicly available on GitHub for independent verification {cite}`dottxt2024demos`.\n", + "\n", + "\n", ".txt team identifies several flaws in the methodology of \"Let Me Speak Freely?\" that they believe led to inaccurate conclusions:\n", "\n", "* The paper finds that structured output improves performance on classification tasks but doesn't reconcile this finding with its overall negative conclusion about structured output. \n", diff --git a/tamingllms/_static/structured_output/rebuttal.png b/tamingllms/_static/structured_output/rebuttal.png new file mode 100644 index 0000000000000000000000000000000000000000..d1a3659f6ba54aadf77bf03b47edc50ad764170a GIT binary patch literal 70249 zcmbTeby$>Z7dMKcvQ>mFC+;$gWrk<&=f2natzSGrkCkPvT%x>$hlh7XPF7MC5AS>@9v=R? ze=fk^BoH|Y!+(UFr8J$@?980qj2unzl#HD1ZS0(FERAounmRgJ+S&533$b&v-m-9Z zws#Wd;6VKM2iWZ#%{gu>;}5}`T(pBzSHp2Zm^Gzbd67LKio}A=^r|yZ%qi*ik 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The .txt team challenges the findings of the paper \"Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models\" {cite}`tam2024letspeakfreelystudy`. The paper claims that structured output formats, like JSON, negatively affect the performance of LLMs, particularly when it comes to reasoning. The rebuttal argues that **structured generation, when done correctly, actually *improves* performance**.\n", "\n", + "\n", + "```{figure} ../_static/structured_output/rebuttal.png\n", + "---\n", + "name: structured_vs_unstructured\n", + "alt: Structured vs Unstructured Results by .txt team\n", + "scale: 60%\n", + "align: center\n", + "---\n", + "Structured vs Unstructured Results by .txt team.\n", + "```\n", + "The .txt team presents compelling evidence through their reproduction of the paper's experiments. While their unstructured results align with the original paper's findings, their structured results paint a dramatically different picture - demonstrating that structured generation actually improves performance (see {numref}`structured_vs_unstructured`). The team has made their experimental notebooks publicly available on GitHub for independent verification {cite}`dottxt2024demos`.\n", + "\n", + "\n", ".txt team identifies several flaws in the methodology of \"Let Me Speak Freely?\" that they believe led to inaccurate conclusions:\n", "\n", "* The paper finds that structured output improves performance on classification tasks but doesn't reconcile this finding with its overall negative conclusion about structured output. \n", diff --git a/tamingllms/references.bib b/tamingllms/references.bib index e123b14..aa9c917 100644 --- a/tamingllms/references.bib +++ b/tamingllms/references.bib @@ -45,3 +45,12 @@ @misc{aider2024codejson howpublished={\url{https://aider.chat/2024/08/14/code-in-json.html}}, note={Accessed: 2024} } + +@misc{dottxt2024demos, + title={Say What You Mean: Demos}, + author={Dottxt}, + year={2024}, + howpublished={\url{https://github.com/dottxt-ai/demos/tree/main/say-what-you-mean}}, + note={Accessed: 2024} +} +