7 ms·
anything that doesn't touch the model parameters at all once it has been compiled. for example, in streaming ASR of an encoder-decoder you can get gains in accu
by bytesandbits 6mo ago
anything that doesn't touch the model parameters at all once it has been compiled. for example, in streaming ASR of an encoder-decoder you can get gains in accuracy just by enhancing the encoder-decoder orchestration and ratio, frequency of fwd passes, dynamically adjusting the length of rolling windows (if using full attention). Prompting would be part of this too, including few-shot examples. Decoding strategy is also part of this (top-k, nucleus, speculative decoding, greedy or anything else). Applying signal processing or any kind of processing to the input before getting it into the model, or to the output. There are a lot of things you can do.
- Linello 6mo agoAlso think about the program-synthesis approach proposed by Poetiq.ai. python programs are being generated and evaluated against previous examples. Then in-context learning is done programmatically via prompt concatenation. If you can "score" online the working and non working examples, then you have a very strong reward signal.