From Form(s) to Meaning: Probing the Semantic Depths of Language Models
  Using Multisense Consistency
Xenia Ohmer Elia Bruni Dieuwke Hupkes
The staggering pace with which the capabilities of large language models
(LLMs) are increasing, as measured by a range of commonly used natural language
understanding (NLU) benchmarks, raises many questions regarding what
"understanding" means for a language model and how it compares to human
understanding. This is especially true since many LLMs are exclusively trained
on text, casting doubt on whether their stellar benchmark performances are
reflective of a true understanding of the problems represented by these
benchmarks, or whether LLMs simply excel at uttering textual forms that
correlate with what someone who understands the problem would say. In this
philosophically inspired work, we aim to create some separation between form
and meaning, with a series of tests that leverage the idea that world
understanding should be consistent across presentational modes - inspired by
Fregean senses - of the same meaning. Specifically, we focus on consistency
across languages as well as paraphrases. Taking GPT-3.5 as our object of study,
we evaluate multisense consistency across five different languages and various
tasks. We start the evaluation in a controlled setting, asking the model for
simple facts, and then proceed with an evaluation on four popular NLU
benchmarks. We find that the model's multisense consistency is lacking and run
several follow-up analyses to verify that this lack of consistency is due to a
sense-dependent task understanding. We conclude that, in this aspect, the
understanding of LLMs is still quite far from being consistent and human-like,
and deliberate on how this impacts their utility in the context of learning
about human language and understanding.