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Nice workaround. I just wish there was a less 'lossy' way to go about it!
by joerick 2y ago
Nice workaround. I just wish there was a less 'lossy' way to go about it!
- jacobr1 2y agoCould you explicitly train a set of embeddings that performed that step in the process? For example which computing the loss, you compare the difference against the normalized text rather than the original. Or alternatively do this as a fine-tuning. Then you would have embedding that optimized for the characteristics you care about.
- hobs 2y agoNormal full text search stuff helps reduce the search space - eg lemming, stemming, query simplification stuff were all way before LLMs.