Contrastive targeted SFT as a mechinterp method - has anyone mapped causal dependency interactions this way? [D]
Evolving story · 1 updatesContrastive SFT ResearchTimeline →A researcher is experimenting with contrastive targeted SFT on a 31B model to improve specific capability dimensions. The goal is to understand causal dependency interactions.
- ›A researcher is experimenting with contrastive targeted SFT on a 31B model.
- ›The goal is to improve specific capability dimensions and understand causal dependency interactions.
- ›The experiment involves training contrastive variants from the same checkpoint.
The researcher's approach involves using targeted SFT to improve specific capability dimensions. They are using a judge to evaluate the model's performance across multiple domains and quality dimensions. The use of contrastive learning is intended to help the model learn to distinguish between different concepts and improve its performance on the weakest dimension. The experiment is ongoing, and the researcher is seeking input from the community on the approach. The use of a large language model and contrastive learning makes this experiment notable, as it has the potential to provide insights into the capabilities and limitations of these models.
Source: Contrastive targeted SFT as a mechinterp method - has anyone mapped causal dependency interactions this way? [D]. Read the full piece at the source.
This research could provide insights into the capabilities and limitations of large language models.
The development of more capable language models could have significant implications for businesses that rely on AI.
Investors in AI startups may be interested in the potential applications of this research.
This research could provide a useful case study for students interested in AI and machine learning.
The general public may be interested in the potential implications of more advanced language models.
- SFT
- Supervised Fine-Tuning, a method for fine-tuning pre-trained language models.
- Contrastive learning
- A method for training models to distinguish between different concepts.
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