Systematicity in GPT -3’s Interpretation of Novel E nglish Noun Compounds (2022)

Abstract: Levin et al. (2019) show experimentally that the interpretations of novel English noun compounds (e.g., stew skillet), while not fully compositional, are highly predictable based on whether the modifier and head refer to artifacts or natural kinds. Is the large language model GPT-3 governed by the same interpretive principles? To address this question, we first compare Levin et al.’s experimental data with GPT-3 generations, finding a high degree of similarity. However, this evidence is consistent with GPT-3 reasoning only about specific lexical items rather than the more abstract conceptual categories of Levin et al.’s theory. To probe more deeply, we construct prompts that require the relevant kind of conceptual reasoning. Here, we fail to find convincing evidence that GPT-3 is reasoning about more than just individual lexical items. These results highlight the importance of controlling for low-level distributional regularities when assessing whether a large language model latently encodes a deeper theory.

## introduction english noun compounds (e.g., birthday cake, toy store) have long been central to debates about the limits of compositionality in natural language semantics. for partee (1995), they are essentially memorized lexical items whose meanings cannot be predicted from their parts. in contrast, @xcite , wisniewski and love (1998), and @xcite identify a range of systematic constraints on compound interpretation, suggesting underlying cognitive and linguistic properties that are more complex and productive than memorization would suggest. more recently, @xcite show that, for large classes of noun compounds, the meanings are highly predictable. their evidence comes from production and comprehension experiments with novel noun compounds (e.g., stew skil-let, swamp squash), for which participant interpretations are highly consistent. these results suggest that, while noun compound meanings are not fully compositional, they are systematic in the sense of @xcite in this paper, we ask whether the large language model (llm) gpt-3 @xcite is similarly systematic in its handling of novel compounds. our comparisons focus on the freeresponse comprehension experiments of @xcite . @xcite s guiding hypothesis is that the observed systematicity traces to two over-arching conceptual hypotheses. the event-related hypothesis says that, for compounds referring to artifacts (e.g., stew skillet), the modifier will tend to convey information about the creation or use of the compound's referent. the essence-related hypothesis says that, for compounds referring to natural kinds (e.g., swamp squash), the modifier will tend to identify essential properties of the compound's referent. in other words, deep conceptual properties of the component words are constraining how people interpret novel compounds. is gpt-3 governed by the same interpretive principles? we report on three experiments seeking to address this question. in experiment 1, we find that, when prompted with novel compounds, gpt-3's behavior is strikingly aligned with the interpretations human participants gave in @xcite s experiments. in experiment 2, we employ the same protocol but with new noun compounds involving a wider range of modifier-head relationships, and we see essentially the same behavior from gpt-3. these results are consistent with the claim that gpt-3 is governed by the event-and essence-related hypotheses, but it is far from conclusive, since the model may simply be relying on regularities in the interpretations of individual words, rather than reasoning about artifacts or natural kinds as abstract conceptual categories. in experiment 3, we seek to decouple these two explanations by constructing prompts in which random strings are defined as natural kinds or artifacts and then used in novel compounds. in this setting, gpt-3 is much less successful, suggesting that experiments 1 and 2 may have confounded statistical distributions of tokens with deeper conceptual understanding. overall, this highlights the importance of controlling for low-level distributional regularities when assessing whether an llm latently encodes a deeper conceptual or linguistic theory. ## experiment 2: free-response with new novel compounds we are unable to determine whether @xcite 's experimental data might have been part of gpt-3's training or fine-tuning data. in addition, their set of compounds covers a relatively small part of the full space defined by the coding manual, which might make the problem artificially easy for the model. to address both of these concerns, we created a new set of compounds that keyed into different parts of ## experiment 3: in-context conceptual reasoning the results of experiments 1 and 2 are consistent with gpt-3's behavior being governed by the event-and essence-related hypotheses at some level, but many other explanations are available. in particular, it could very well be that gpt-3 is reasoning about individual lexical items without truly ## discussion and conclusion this paper explored gpt-3's handling of novel english endocentric noun compounds, building on psycholinguistic research by @xcite . experiments 1 and 2 suggested that gpt-3 might be governed by the same over-arching conceptual and linguistic principles that shape human interpretations of these forms. however, experiment 3 probed that conclusion more deeply and failed to find evidence for this stronger claim: gpt-3 may instead be memorizing token distributions rather than reasoning about the underlying conceptual categories on which @xcite 's theory depends.