Meta, about the self-rewarding language model (SRLM).
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Meta Research has introduced a revolutionary self-rewarding language model that has GPT-4-level performance. This model presents a new way for language models to continuously improve by evaluating their quality and generating rewards themselves, rather than the traditional way in which existing language models train reward models based on human preferences.
Core principles of the self-rewarding language model (SRLM):
Self-Instruction Creation: The model acts as a model that follows instructions to generate useful and high-quality responses to user queries, while simultaneously generating and evaluating new instructions to expand the training data set.
Self-evaluation: The model evaluates and rewards the responses it generates. This allows the model to continuously improve its performance.
Training Process:
Direct Preference Optimization (DPO): The model is trained through an iterative framework called DPO. In each iteration, the model generates candidate responses to a question and uses a large-scale language model (LLM) as a judge to evaluate the quality of the responses.
Self-supervised training: The preferred dataset generated through this process is used to train the next model iteration, reinforcing the ability of both response generation and reward modeling.
Performance?
This model outperforms models such as Claude 2, Gemini Pro, and GPT-4 0613 on the AlpacaEval 2.0 benchmark with 3-repetitive training .
By incorporating a self-reward mechanism, this language model opens up the possibility of continuous improvement, free from the constraints of a fixed reward model. Although this effect may have limitations in real-world settings, the potential for obtaining superior reward models and language models is very exciting.
Features and benefits of the RAG (Retrieval-Augmented Generation) model:
Information retrieval integration: The RAG model uses large databases to retrieve relevant information to solve problems. This allows the model to generate more accurate and detailed answers.
Improved response quality: Since answers are generated based on the retrieved information, the generated text is more accurate and relevant.
Flexibility and scalability: You can create customized answers to different types of questions and quickly adapt to new domains.
Features and Benefits of Self-Rewarding Language Models:
Self-improvement mechanism: Self-rewarding language models continuously improve by evaluating and rewarding their own performance. This allows the model to improve its performance without human evaluation.
Efficient learning process: Instead of humans preparing and evaluating training data, the model generates and optimizes training data on its own. This makes the training process more efficient and faster.
Overcoming Human Performance Limits: While traditional methods have limitations in human evaluation performance, the self-reward model goes beyond this and aims for superhuman performance.
Continuous improvement through self-evaluation: The model has a structure that continuously improves its performance through repeated learning by self-evaluation and rewarding its own answers.
Interaction and synergy of the two models:
The RAG model and the self-reward model can improve the performance of the language model by taking advantage of each other's strengths. Combining the search-based information provided by the RAG model and the continuous improvement ability of the self-reward model, the model can generate more accurate, detailed, and creative answers.
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