Reflection 70B: A Ground Breaking Open-Source LLM, Trained with a New Technique called Reflection-Tuning that Teaches a LLM to Detect Mistakes in Its Reasoning and Correct Course

Practical Solutions for Mitigating Hallucinations in AI Systems

Introduction

Large language models (LLMs) sometimes produce incorrect, misleading, or nonsensical information, which can have serious consequences in high-stakes applications like medical diagnosis or legal advice. Minimizing these errors is crucial for ensuring trustworthiness and reliability in AI systems.

Reflection-Tuning Approach

A novel approach called “Reflection-Tuning” has been introduced to address the issue of hallucinations in LLMs. This approach, integrated into the Reflection 70B model, enables the model to reflect on its reasoning during the output generation process, improving accuracy and consistency.

Model Functionality

Reflection 70B adds distinct phases of reasoning and reflection using special tokens. It outputs its thought process inside special tags and revises potential errors with tags before presenting a refined answer inside tags. This allows the model to catch mistakes before providing the user with a final answer, reducing hallucinations and increasing trust.

Training Methodology

Reflection-Tuning uses a form of self-supervised learning to train the model to pause, analyze its thought process, and correct errors before responding. The training involves prompt generation, response generation, reflection on the responses, and refinement based on the reflection, providing the model with the ability to evaluate the quality of its own answers.

Performance and Reliability

Reflection 70B has shown significant improvements in mitigating hallucinations, outperforming other models in benchmarks such as MMLU, MATH, and IFEval. It achieved 89.9% on MMLU, 79.7% on MATH, and 90.1% on IFEval, confirming its effectiveness. Additionally, it was checked for contamination using LMSys’s LLM Decontaminator, ensuring its reliability and robustness.

Conclusion

Reflection 70B introduces a practical approach to mitigating hallucinations in LLMs through the Reflection-Tuning technique, successfully reducing errors and increasing the reliability of its responses. While promising, further research and improvement are needed in handling more complex hallucinations.

AI Solutions for Business

Reflection 70B offers practical solutions for evolving companies with AI, ensuring competitiveness and leveraging AI for business advantage. It can redefine work processes, identify automation opportunities, define KPIs, select AI solutions, and implement AI usage gradually for impactful business outcomes.

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