Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand? (2021.tacl-1)
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| Challenge: | Language models trained on billions of tokens have recently led to unprecedented results on many NLP tasks. |
| Approach: | They examine whether assertions enable a system to emulate representations preserving semantic relations like equivalence. |
| Outcome: | The proposed model can emulate representations preserving semantic relations like equivalence, but it can become uncomputable for classes of languages where expressions can take different values in different contexts. |
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| Challenge: | People rely heavily on context to enrich meaning beyond what is literally said. |
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| Challenge: | a priori, large neural language models are described as understanding or capturing meaning on tasks that are ostensibly meaningsensitive. |
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| Challenge: | Existing studies on explainable AI focus on post-hoc explanation methods that interpret trained models through external approximations. |
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AND does not mean OR: Using Formal Languages to Study Language Models’ Representations (2021.acl-short)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
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Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
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