There are a bunch of approaches that do this kind of thing to reduce token usage ("semble" came to mind, technically different but functionally similar) but their performance is usually mixed because the models haven't…
Enterprises still have big contracts with github, those companies are imposing tight spending limits now and if the open weight models enable those limits to last a bit longer that's probably quite popular.
Everything is currently pointing towards inference being the main cost driver for LLMs in the future. Test-time-compute requires huge amounts of tokens in inference and makes providing frontier models as services…
Interesting! So did you do any experiments on a relevant subset of the data to test whether LLM performance degrades by introducing a new, presumably unknown to the LLM, format?
Accepting the possibility of committing the old "solving social problems with technological solutions" fallacy: I wonder if an offtopic channel without history (or only a very limited one) could help here. Something…
There are a bunch of approaches that do this kind of thing to reduce token usage ("semble" came to mind, technically different but functionally similar) but their performance is usually mixed because the models haven't…
Enterprises still have big contracts with github, those companies are imposing tight spending limits now and if the open weight models enable those limits to last a bit longer that's probably quite popular.
Everything is currently pointing towards inference being the main cost driver for LLMs in the future. Test-time-compute requires huge amounts of tokens in inference and makes providing frontier models as services…
Interesting! So did you do any experiments on a relevant subset of the data to test whether LLM performance degrades by introducing a new, presumably unknown to the LLM, format?
Accepting the possibility of committing the old "solving social problems with technological solutions" fallacy: I wonder if an offtopic channel without history (or only a very limited one) could help here. Something…