on Toshareproject.it - curated by Bruce Sterling
*Maybe the most Artmaker Blog headline ever.
*Unfortunately these language-model AIs are not yet teaching themselves to use Leatherman multitools. They’re merely using application-interfaces for other software “tools.” That seems significant, however.
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or fac- tual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. Toolformer achieves substan- tially improved zero-shot performance across a variety of downstream tasks, often competi- tive with much larger models, without sacrificing its core language modeling abilities.
We explore a variety of tools to address different shortcomings of regular LMs. The only constraints we impose on these tools is that (i) both their inputs and outputs can be represented as text sequences, and (ii) we can obtain a few demonstrations of their intended use. Concretely, we explore the fol- lowing five tools: a question answering system, a Wikipedia search engine, a calculator, a calendar, and a machine translation system. Some examples of potential calls and return strings for the APIs associated with each of these tools are shown in Table 1. We briefly discuss all tools below; further details can be found in Appendix A….