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The world's strongest open source model: Reflection 70B . It is trained using a technique called " Reflection-Tuning ", which teaches the model to find its own mistakes during reasoning and correct itself. Reflection 70B surpasses top closed source models (such as GPT-4o and Claude 3.5 Sonnet) on multiple benchmarks (MMLU, MATH, IFEval, GSM8K) and beats Llama 3.1 405B.
The model improves the effectiveness of chain thinking (CoT) by separating the planning process into independent steps and ensures that the output is concise and clear. In addition, the development team ensures the decontamination of the data.
The Reflection 70B weight has already been released, and the 405B version will be available next week, which is expected to improve performance further.
Features of Reflection 70B
1. Reflection-Tuning
The model introduces reflective tuning technology , which enables it to detect and correct its own reasoning errors during the reasoning process. This feature helps the model proactively identify problems and make corrections before generating the final answer, thereby improving the accuracy of the answer.
When the model generates an answer, it outputs its reasoning and
<thinking> surrounds the thought process with special tags (such as ).When the model detects an inference error during inference, it marks
<reflection> the error with a label and corrects itself. This feature enhances the reliability of the model, especially when dealing with complex problems.This enables the model to dynamically adjust its answers, reducing errors and ensuring higher accuracy.
2. Separation of reasoning process
When the model generates an answer, it separates the reasoning process from the final answer, using
<thinking><output> the label to output the reasoning content and the label to output the final answer. This separation method improves transparency and allows users to clearly understand the reasoning logic of the model.The model is particularly good at handling complex reasoning tasks. By using system prompts, the model is able to effectively complete highly logical queries and provide accurate and reflective answers.
3. Compatible with Llama 3.1 chat format
The model is trained based on the Llama 3.1 70B InstructLlama 3.1 and uses the standard chat format. This means that users can use this model like other Llama models, and its training process also adds some special tags to enhance reasoning and reflection capabilities.
4. Customizable system prompts
Reflection Llama-3.1 70B uses system prompts to guide the model's reasoning and self-reflection. Users can adjust these prompts to customize the model's behavior as needed. For example, prompt the model to think carefully or proactively correct errors when they occur.
5. Special training data
The model was trained using synthetic data generated by Glaive, which helped improve the model's reasoning capabilities in a variety of tasks.
Prompt
- ”World-Class AI System”: This part tells the model that it has a high level of reasoning and reflection capabilities, thereby activating its complex reasoning capabilities.
- Reasoning process
<thinking>: The model is required
to perform detailed reasoning within the tag. This allows users to clearly see how the model handles the problem step by step.
- Self-correction
<reflection>: If the model detects an error during inference, it will
mark and correct the error within the label, which embodies the key capability of reflective tuning techniques.
- Final answer
<output>: The model provides the final answer in the tag after inference and possible corrections .
Background of Reflection 70B
Reflection-Tuning is an emerging machine learning technique for improving the reasoning capabilities of large language models (LLMs). The core idea of this technique is to teach the model to self-detect and correct errors during reasoning. Specifically, the model will mark its own thinking process in the process of generating answers, and reflect and correct it when errors are detected.
Working principle:
- Thought process labeling
<thinking></thinking>: Before generating an answer, the model outputs its reasoning process and surrounds it with special labels such as and tags. This labeling helps the model distinguish the thought process from the final answer.
- Reflection labels
<reflection>: When the model detects an error in its reasoning, it corrects itself in a marked “reflection” area (e.g.,
label). This process enables the model to make logical adjustments before giving a final answer.
- Final answer
<output>: After correcting errors during reasoning, the model generates a final answer and
surrounds it with special markers (such as a tag) to ensure that users get accurate results.
Advantages:
- Error detection and correction: This technology enables the model to self-check and correct errors during the generation process, significantly improving the accuracy of the answers.
- Transparent reasoning: Users are able to see the model’s thought process, which helps them understand how the model reached its conclusions.
- Dynamic Improvement: By reflecting on labels, the model can more flexibly adjust the reasoning process when generating answers, reducing the occurrence of errors.
Model download: https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B
- Author:KCGOD
- URL:https://kcgod.com/Reflection-Llama-3.1-70B
- Copyright:All articles in this blog, except for special statements, adopt BY-NC-SA agreement. Please indicate the source!
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