Strategic Account Growth Specialist
Posted: Thu Dec 26, 2024 4:40 am
) Innovation Points Iterative Guidance: It does not need to build a large-scale reasoning chain dataset, but guides the LLM to generate its own reasoning chain through a small number of examples to achieve self-improvement. Rationalization Technology: It introduces rationalization technology to help the LLM generate new reasoning chains by providing correct answers as reminders, overcoming the limitations of training for further thinking, and learning opportunities from failure cases. ) Advantages of -a Improve reasoning abilities: It can effectively improve the performance of the LLM on complex tasks such as mathematical reasoning and common sense reasoning. Reduce data requirements: It does not require large datasets of the inference chain, reducing the difficulty and cost of data acquisition.
Improved robustness: It allows LLM to brazil email list learn from failure cases and improve its robustness on complex problems. ) Limitations of -a Initial model requirements: It requires the initial model to have certain inference capabilities, otherwise it will be difficult to start the training process. Dependence on few-shot examples: It is heavily dependent on a small number of Few-Shot inference examples in inference tasks, resulting in limited inference capabilities of the model and difficulty in dealing with complex and large-scale tasks. Limited generalization ability: Although it can improve the reasoning ability of the model through iteration, its application is mainly limited to specific structured tasks (such as answering questions), and it is difficult to achieve the same effect in open domains or arbitrary text generation tasks.
Impact of data quality: The performance of is affected by the quality of the initial reasoning chain. Interpretation fidelity: The reasoning chain it generates may not fully reflect the internal reasoning process of the LLM, and there is also an issue of interpretation fidelity. 5) Similarities between and reinforcement learning goals Iterative updating: Both and reinforcement learning use iterative methods to update the model and continuously optimize its performance. Reward signal: generatescan effectively improve the performance of LLM on complex tasks such as mathematical reasoning and common sense reasoning. Reduce data requirements: It does not require large data sets of the inference chain, reducing the difficulty and cost of data acquisition.
Improved robustness: It allows LLM to brazil email list learn from failure cases and improve its robustness on complex problems. ) Limitations of -a Initial model requirements: It requires the initial model to have certain inference capabilities, otherwise it will be difficult to start the training process. Dependence on few-shot examples: It is heavily dependent on a small number of Few-Shot inference examples in inference tasks, resulting in limited inference capabilities of the model and difficulty in dealing with complex and large-scale tasks. Limited generalization ability: Although it can improve the reasoning ability of the model through iteration, its application is mainly limited to specific structured tasks (such as answering questions), and it is difficult to achieve the same effect in open domains or arbitrary text generation tasks.
Impact of data quality: The performance of is affected by the quality of the initial reasoning chain. Interpretation fidelity: The reasoning chain it generates may not fully reflect the internal reasoning process of the LLM, and there is also an issue of interpretation fidelity. 5) Similarities between and reinforcement learning goals Iterative updating: Both and reinforcement learning use iterative methods to update the model and continuously optimize its performance. Reward signal: generatescan effectively improve the performance of LLM on complex tasks such as mathematical reasoning and common sense reasoning. Reduce data requirements: It does not require large data sets of the inference chain, reducing the difficulty and cost of data acquisition.