Tentative GPT4′s summary. This is part of an experiment. Up/Downvote “Overall” if the summary is useful/harmful. Up/Downvote “Agreement” if the summary is correct/wrong. If so, please let me know why you think this is harmful. (OpenAI doesn’t use customers’ data anymore for training, and this API account previously opted out of data retention)
TLDR: The article explores pruning techniques in large language models (LLMs) to separate code-writing and text-writing capabilities, finding moderate success (up to 75%) and suggesting that attention heads are task-general while feed-forward layers are task-specific.
Arguments: - The author attempts to prune LLMs to exclusively retain or remove coding abilities, using next-token prediction on Pile, Code, and Python datasets as proxies for general tasks. - Pruning methods focus on MLP and attention blocks, with random removal as a baseline. - Different metrics were tested for pruning, including calculating importance functions based on activation frequency or standard deviation, and applying singular value decomposition (SVD).
Takeaways: - LLMs have some level of separability between tasks with basic pruning methods, especially in larger models. - Attention heads appear more task-general, while feed-forward layers appear more task-specific. - There’s room for more advanced separability techniques and training LLMs to be more separable from the start.
Strengths: - The article provides empirical evidence for separability in LLMs and explores both feed-forward and attention layers, contributing to a comprehensive understanding of the modularity in LLMs. - The pruning procedures and evaluation metrics used effectively illustrate the differences between targeted and random pruning. - The exploration of various pruning methods and importance functions yields insights into the efficacy of different strategies.
Weaknesses: - The next-token prediction metric is limited in truly understanding task separability. - Only a few datasets were used, limiting generalizability. - The author acknowledges their limited statistical background, which may affect the quality of the tests and metrics used.
Interactions: - The article’s findings on separability in LLMs may be linked to AI alignment and ensuring AGIs have appropriate goals. - The research could connect to the concept of modularity in deep learning, mixture of experts architectures, and other AI safety research areas.
Factual mistakes: - None identified in the provided summary content.
Missing arguments: - The summary could explore other potential pruning methods, such as those using sparsity penalties or nudge-based interventions. - More analysis on the relationship between modularity and model size could provide further insights into relevant AI alignment topics.
Tentative GPT4′s summary. This is part of an experiment.
Up/Downvote “Overall” if the summary is useful/harmful.
Up/Downvote “Agreement” if the summary is correct/wrong.
If so, please let me know why you think this is harmful.
(OpenAI doesn’t use customers’ data anymore for training, and this API account previously opted out of data retention)
TLDR:
The article explores pruning techniques in large language models (LLMs) to separate code-writing and text-writing capabilities, finding moderate success (up to 75%) and suggesting that attention heads are task-general while feed-forward layers are task-specific.
Arguments:
- The author attempts to prune LLMs to exclusively retain or remove coding abilities, using next-token prediction on Pile, Code, and Python datasets as proxies for general tasks.
- Pruning methods focus on MLP and attention blocks, with random removal as a baseline.
- Different metrics were tested for pruning, including calculating importance functions based on activation frequency or standard deviation, and applying singular value decomposition (SVD).
Takeaways:
- LLMs have some level of separability between tasks with basic pruning methods, especially in larger models.
- Attention heads appear more task-general, while feed-forward layers appear more task-specific.
- There’s room for more advanced separability techniques and training LLMs to be more separable from the start.
Strengths:
- The article provides empirical evidence for separability in LLMs and explores both feed-forward and attention layers, contributing to a comprehensive understanding of the modularity in LLMs.
- The pruning procedures and evaluation metrics used effectively illustrate the differences between targeted and random pruning.
- The exploration of various pruning methods and importance functions yields insights into the efficacy of different strategies.
Weaknesses:
- The next-token prediction metric is limited in truly understanding task separability.
- Only a few datasets were used, limiting generalizability.
- The author acknowledges their limited statistical background, which may affect the quality of the tests and metrics used.
Interactions:
- The article’s findings on separability in LLMs may be linked to AI alignment and ensuring AGIs have appropriate goals.
- The research could connect to the concept of modularity in deep learning, mixture of experts architectures, and other AI safety research areas.
Factual mistakes:
- None identified in the provided summary content.
Missing arguments:
- The summary could explore other potential pruning methods, such as those using sparsity penalties or nudge-based interventions.
- More analysis on the relationship between modularity and model size could provide further insights into relevant AI alignment topics.