Pragmatic inference of scalar implicature by LLMs
This study investigates how Large Language Models (LLMs), particularly BERT (Devlin et al., 2019) and GPT-2 (Radford et al., 2019), engage in pragmatic inference of scalar implicature, such as some. Two sets of experiments were conducted using cosine similarity and next sentence/token prediction as...
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Zusammenfassung: | This study investigates how Large Language Models (LLMs), particularly BERT
(Devlin et al., 2019) and GPT-2 (Radford et al., 2019), engage in pragmatic
inference of scalar implicature, such as some. Two sets of experiments were
conducted using cosine similarity and next sentence/token prediction as
experimental methods. The results in experiment 1 showed that, both models
interpret some as pragmatic implicature not all in the absence of context,
aligning with human language processing. In experiment 2, in which Question
Under Discussion (QUD) was presented as a contextual cue, BERT showed
consistent performance regardless of types of QUDs, while GPT-2 encountered
processing difficulties since a certain type of QUD required pragmatic
inference for implicature. The findings revealed that, in terms of theoretical
approaches, BERT inherently incorporates pragmatic implicature not all within
the term some, adhering to Default model (Levinson, 2000). In contrast, GPT-2
seems to encounter processing difficulties in inferring pragmatic implicature
within context, consistent with Context-driven model (Sperber and Wilson,
2002). |
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DOI: | 10.48550/arxiv.2408.06673 |