Toolformer: Language Models Can Teach Themselves to Use Tools
Schick et al., 2023, Toolformer (Advances in Neural Information Processing Systems 36 (NeurIPS 2023))
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6 Limitations
- What are Toolformer’s main limitations?It can’t chain tools (calls were sampled independently, one per example), can’t use tools interactively (e.g. refine a search), is sensitive to the input wording when deciding to call, is sample-inefficient for some tools (a million documents yield a few thousand useful calculator calls), and ignores the cost of calls.
- Schick et al., 2023, Toolformer (Advances in Neural Information Processing Systems 36 (NeurIPS 2023)), §6
Source
- Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda and Thomas Scialom. 2023. Toolformer: Language Models Can Teach Themselves to Use Tools. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023). https://papers.nips.cc/paper_files/paper/2023/hash/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html