Current LLMs have serious limitations in adaptability, flexibility, and continuous learning. The knowledge and world model contained in the LLM’s parameters is fixed at the time of training completion. Interactions during inference time do not influence the model’s knowledge:
Factual knowledge is outdated: limited to the last date of the training data.
No learning from conversations: new knowledge obtained in interactions isn’t incorporated into the model.
Limited user adaptation: customization for users relies on side memory, which only modifies pre-prompts without internalizing changes.
Static in specific use cases: models do not learn from day-to-day interactions in their designated environments.
No learning from physical interactions: models used in physical environments (e.g., “robots”) do not learn from their experiences.
In all these cases, an incorrect answer from the model will repeat time and again because the model is fixed.
These limitations cause incorrect answers to be repeated since the model is static. Current solutions attempt to address these issues but have drawbacks:
Web access for updates.
Redundant searches for the same data in each interaction.
Periodic re-training.
Expensive process of identifying errors and re-training the model.
Side memory for important information.
Limitation in memory size for each prompt (attention problems and cost of inference).
PROPOSAL FOR DYNAMIC KNOWLEDGE ACQUISITION
We propose a more dynamic approach to knowledge acquisition, automating the incorporation of new pieces of knowledge through three main processes.
Automated identification of limitations or errors.
Automated generation of training data to correct identified issues.
Short training cycles to integrate new data to the model.
Automated identification of limitations or errors
The model can identify limitations or errors through various methods:
Answering outdated questions: the model identifies missing factual data and searches trusted web sources for accurate answers.
User interactions: during conversations, the model can recognize and correct its initial errors with high certainty.
Expert feedback: trusted professionals in different fields can provide verified information that conflicts with existing model knowledge.
Dedication of more inference time: allocating more inference time of one or various models interacting together can lead to complex answers not initially available to the main model.
Physical task failures: In a physical environment, task failures indicate the need for algorithmic adaptations.
When the model realizes that there is a new piece of knowledge that should be incorporated into the model, it is stored for the next process.
Automated generation of training data to correct identified issues
We have already seen that a model can be trained to produce good training data for other models, whether it is for a general purpose LLM or for a physical device (Eureka).
Given the identified piece of knowledge to incorporate, the training data to fix that knowledge into the model can be automatically generated.
The atomization of knowledge elements to incorporate will limit the training dataset size.
Short training cycles to integrate new data to the model
With the training data automatically generated, periodic and frequent training cycles will update the main model to incorporate new knowledge efficiently.
BENEFITS AND DRAWBACKS OF THIS APPROACH
Benefits:
Continuous learning and adaptation: the model continuously improves its knowledge base through real-time interactions, user feedback, and physical environment experiences, leading to a more accurate and up-to-date model.
Reduced redundancy: by automating the incorporation of new knowledge, the need for redundant web searches and periodic retraining is minimized, saving computational resources and time.
Enhanced user experience: personalized responses and improvements based on user interactions lead to a better user experience as the model adapts to individual user needs.
Scalability: the automated generation of training data and short training cycles make the approach scalable, allowing it to be applied to various domains and use cases efficiently.
Increased reliability: identifying and correcting errors through multiple methods, including expert feedback and dedicated inference time, increases the overall reliability and accuracy of the model.
In short, the models will learn from their interactions, as well as any other intelligent creature does in nature.
Drawbacks:
Resource Intensive: implementing continuous learning and automated training processes requires significant computational resources, which may be costly and energy-intensive.
Potential for misinformation: If the sources of new information are not carefully vetted, there is a risk of incorporating inaccurate or biased data into the model.
Security and privacy concerns: continuous learning from user interactions and web searches raises concerns about data security and user privacy, requiring robust measures to protect sensitive information and giving the users the option to opt-out from this scheme.
Unintended deviation of the model: updates in the model incorporating new knowledge can have unintended consequences in parts of the model that have no relationship with the updated information.
In summary, the changing nature of the model will require continuous checks to guarantee its security and alignment.
FLEXIBLE AND ADAPTABLE LLM’s WITH CONTINUOUS SELF TRAINING
CURRENT UNFLEXIBLE MODELS
Current LLMs have serious limitations in adaptability, flexibility, and continuous learning. The knowledge and world model contained in the LLM’s parameters is fixed at the time of training completion. Interactions during inference time do not influence the model’s knowledge:
Factual knowledge is outdated: limited to the last date of the training data.
No learning from conversations: new knowledge obtained in interactions isn’t incorporated into the model.
Limited user adaptation: customization for users relies on side memory, which only modifies pre-prompts without internalizing changes.
Static in specific use cases: models do not learn from day-to-day interactions in their designated environments.
No learning from physical interactions: models used in physical environments (e.g., “robots”) do not learn from their experiences.
In all these cases, an incorrect answer from the model will repeat time and again because the model is fixed.
These limitations cause incorrect answers to be repeated since the model is static. Current solutions attempt to address these issues but have drawbacks:
Web access for updates.
Redundant searches for the same data in each interaction.
Periodic re-training.
Expensive process of identifying errors and re-training the model.
Side memory for important information.
Limitation in memory size for each prompt (attention problems and cost of inference).
PROPOSAL FOR DYNAMIC KNOWLEDGE ACQUISITION
We propose a more dynamic approach to knowledge acquisition, automating the incorporation of new pieces of knowledge through three main processes.
Automated identification of limitations or errors.
Automated generation of training data to correct identified issues.
Short training cycles to integrate new data to the model.
Automated identification of limitations or errors
The model can identify limitations or errors through various methods:
Answering outdated questions: the model identifies missing factual data and searches trusted web sources for accurate answers.
User interactions: during conversations, the model can recognize and correct its initial errors with high certainty.
Expert feedback: trusted professionals in different fields can provide verified information that conflicts with existing model knowledge.
Dedication of more inference time: allocating more inference time of one or various models interacting together can lead to complex answers not initially available to the main model.
Physical task failures: In a physical environment, task failures indicate the need for algorithmic adaptations.
When the model realizes that there is a new piece of knowledge that should be incorporated into the model, it is stored for the next process.
Automated generation of training data to correct identified issues
We have already seen that a model can be trained to produce good training data for other models, whether it is for a general purpose LLM or for a physical device (Eureka).
Given the identified piece of knowledge to incorporate, the training data to fix that knowledge into the model can be automatically generated.
The atomization of knowledge elements to incorporate will limit the training dataset size.
Short training cycles to integrate new data to the model
With the training data automatically generated, periodic and frequent training cycles will update the main model to incorporate new knowledge efficiently.
BENEFITS AND DRAWBACKS OF THIS APPROACH
Benefits:
Continuous learning and adaptation: the model continuously improves its knowledge base through real-time interactions, user feedback, and physical environment experiences, leading to a more accurate and up-to-date model.
Reduced redundancy: by automating the incorporation of new knowledge, the need for redundant web searches and periodic retraining is minimized, saving computational resources and time.
Enhanced user experience: personalized responses and improvements based on user interactions lead to a better user experience as the model adapts to individual user needs.
Scalability: the automated generation of training data and short training cycles make the approach scalable, allowing it to be applied to various domains and use cases efficiently.
Increased reliability: identifying and correcting errors through multiple methods, including expert feedback and dedicated inference time, increases the overall reliability and accuracy of the model.
In short, the models will learn from their interactions, as well as any other intelligent creature does in nature.
Drawbacks:
Resource Intensive: implementing continuous learning and automated training processes requires significant computational resources, which may be costly and energy-intensive.
Potential for misinformation: If the sources of new information are not carefully vetted, there is a risk of incorporating inaccurate or biased data into the model.
Security and privacy concerns: continuous learning from user interactions and web searches raises concerns about data security and user privacy, requiring robust measures to protect sensitive information and giving the users the option to opt-out from this scheme.
Unintended deviation of the model: updates in the model incorporating new knowledge can have unintended consequences in parts of the model that have no relationship with the updated information.
In summary, the changing nature of the model will require continuous checks to guarantee its security and alignment.