Critical Thinking for Language Models Gregor Betz Christian Voigt Kyle Richardson KIT KIT Allen Institute for AI Karlsruhe, Germany Karlsruhe, Germany Seattle, WA, USA
[email protected] [email protected] [email protected] Abstract quality of online debates (Hansson, 2004; Guia¸su and Tindale, 2018; Cheng et al., 2017): Neural lan- This paper takes a first step towards a critical guage models are known to pick up and reproduce thinking curriculum for neural auto-regressive normative biases (e.g., regarding gender or race) language models. We introduce a synthetic present in the dataset they are trained on (Gilburt corpus of deductively valid arguments, and generate artificial argumentative texts to train and Claydon, 2019; Blodgett et al., 2020; Nadeem CRiPT: a critical thinking intermediarily pre- et al., 2020), as well as other annotation artifacts trained transformer based on GPT-2. Signifi- (Gururangan et al., 2018); no wonder this happens cant transfer learning effects can be observed: with argumentative biases and reasoning flaws, too Trained on three simple core schemes, CRiPT (Kassner and Schütze, 2020; Talmor et al., 2020). accurately completes conclusions of different, This diagnosis suggests that there is an obvious and more complex types of arguments, too. remedy for LMs’ poor reasoning capability: make CRiPT generalizes the core argument schemes in a correct way. Moreover, we obtain con- sure that the training corpus contains a sufficient sistent and promising results for NLU bench- amount of exemplary episodes of sound reasoning. marks. In particular, CRiPT’s zero-shot accu- In this paper, we take a first step towards the cre- racy on the GLUE diagnostics exceeds GPT- ation of a “critical thinking curriculum” for neural 2’s performance by 15 percentage points.