This post discusses the limitations of using cosine similarity for compatibility matching, specifically in the context of a dating app. The author found that high cosine similarity scores didn't always translate to actual compatibility due to the inability of embeddings to capture dealbreaker preferences. They improved results by incorporating structured features and hard filters.
A post with pithy observations and clear conclusions from building complex LLM workflows, covering topics like prompt chaining, data structuring, model limitations, and fine-tuning strategies.
BrisquelyBrusque writes "I think what he's getting at is, we'll never have an algorithm that is
1. fast, distributed, easily deployed
2. interpretable
3. able to converge quickly for most problems
4. robust to noise, outliers, multicollinearity, class imbalance, and the curse of dimensionality
5. optimized for any combination of numeric variables and factors
6. self-supervised (no need for extensive parameter tuning)
7. capable of probability estimates as well as predictions
8. able to issue predictions for multiple targets
9. comfortable with structured, unstructured data (text, 2D, 3D, audio, tabular)
10. open-source
Besides, a recent analysis by Amazon Web Services found that 50 to 95% of all ML applications in an organization are based on traditional ML (random forests, regression models). That's why these application papers matter -- we're learning to make progress in certain areas where traditional ML fails."
1. fast, distributed, easily deployed
2. interpretable
3. able to converge quickly for most problems
4. robust to noise, outliers, multicollinearity, class imbalance, and the curse of dimensionality
5. optimized for any combination of numeric variables and factors
6. self-supervised (no need for extensive parameter tuning)
7. capable of probability estimates as well as predictions
8. able to issue predictions for multiple targets
9. comfortable with structured, unstructured data (text, 2D, 3D, audio, tabular)
10. open-source
Besides, a recent analysis by Amazon Web Services found that 50 to 95% of all ML applications in an organization are based on traditional ML (random forests, regression models). That's why these application papers matter -- we're learning to make progress in certain areas where traditional ML fails."
