Is artificial intelligence still intelligence? LLM s generalize to novel adjective-noun pairs, but don’t mimic the full human distribution (2024)

Abstract: Inferences from adjective-noun combinations like “Is artificial intelligence still intelligence?” provide a good test bed for LLMs’ understanding of meaning and compositional generalization capability, since there are many combinations which are novel to both humans and LLMs but nevertheless elicit convergent human judgments. We study a range of LLMs and find that the largest models we tested are able to draw human-like inferences when the inference is determined by context and can generalize to unseen adjective-noun combinations. We also propose three methods to evaluate LLMs on these inferences out of context, where there is a distribution of human-like answers rather than a single correct answer. We find that LLMs show a human-like distribution on at most 75% of our dataset, which is promising but still leaves room for improvement.

## introduction as the pretraining datasets of llms grow, it becomes increasingly difficult to test whether llms can generalize to unseen instances of linguistic phenomena, since it is hard to systematically exclude them from the pretraining data @xcite . adjective-noun combinations, in particular with socalled privative adjectives like fake, provide a good test bed for testing granular language understanding and semantic composition in llms, since we can find many adjective-noun bigrams which are easy for humans to understand despite being novel to them, and can further confirm that they are novel to the llm by a string search over its pretraining corpus. adjective-noun bigrams are naturally associated with membership inferences which allow us to test whether llms have successfully "composed" their meaning: is an {adjective} {noun} still a {noun}? this inference is not as straightforward as it may seem: while a yellow flower is clearly still a flower (a subsective inference), a fake or counterfeit dollar bill is typically not a dollar bill (a privative inference), even though a fake watch is typically a watch @xcite . in order to draw the correct inference, humans and llms need to grasp not only the effect of the adjective on the noun's properties, but also which properties are required vs. merely typical for membership in that noun/category. moreover, like many cases of meaning, this inference depends on context. for example, a fake crowd might qualify as a crowd if it is made up of paid actors, but less so if it is just painted dummies on a movie set. nonetheless, humans derive convergent inferences for many novel bigrams both with and without context, giving a ground truth to which we can compare llms. building on the dataset of english human judgments developed in @xcite , we compare llms of several sizes with humans for cases in which the context determines the inference. we further explore three methods to evaluate llms against the full distribution of human ratings when there is no provided context. we find that when the context determines the inference, recent larger llms closely match human behavior, while smaller llms only sometimes do so. almost all llms, even smaller ones, are able to handle novel adjective-noun combinations as well as they handle known ones, suggesting that they evaluate these combinations on-the-fly and can generalize accordingly like humans do. this alignment in performance on observed and novel combinations continues to hold in a setting where no context is provided. however, we find that even 70b parameter llms are only able to approximate the distribution of ratings of a population of human raters for 75% of our dataset or less, struggling on combinations with high human variance and inferences which are rare for a particular adjective, such as a homemade cat not necessarily being a cat. most llms also assign a more positive rating to the question "is artificial intelligence still intelligence?" than most humans (see figure 5 ). in sum, while our generalization results are highly promising, there is room for improvement on the task of matching human inferences in this category overall. we share our code and data on github. foot_0 ## related work most previous computational work on adjectivenoun composition focuses on distributional semantics using word embeddings @xcite @xcite @xcite . @xcite specifically analyze privative adjective embeddings, finding no difference between their embeddings and embeddings of other adjectives. results for early llms are largely negative: bert @xcite shows no evidence of compositionality for adjective-noun and noun-noun phrases, relying instead on word overlap heuristics for similarity judgments @xcite . @xcite study the inferences of adjective-noun combinations for bert and roberta @xcite . they divide adjectives into three inference classes-intersective, subsective and privative-based on previous computational work @xcite , and test whether llms can draw the correct pattern of inferences for adjectives in each class. however, recent work in linguistics @xcite @xcite suggests that bertolini et al.'s task may be ill-defined, since adjectives in these "classes" can license either a subsective or a privative inference depending on the noun and context. indeed, @xcite find that bert can distinguish between intersective and subsective adjectives, which are more clearly distinct. for the "is an x a y" task more broadly, llms from bert to llama 2 @xcite show some, but limited abilities to judge "is an x a y" for hypernyms, especially with negation @xcite @xcite @xcite . results from property learning also show that earlier models struggle to learn what properties are typical of nouns @xcite @xcite )-a key part of our task, since llms must know what constitutes a watch in order to judge whether a fake watch counts as one. meanwhile, lyu et al. (2022) find that bert and gpt-3 (brown et al., 2020) cannot handle inferences on recursive adjectives, such as "is my favorite new movie my favorite movie?", while sathe et al. (2024) find that gpt-2 @xcite , mpt 30b (mosaicml nlp team, 2023) and other smaller models struggle to predict whether rare adjective-noun combinations are acceptable to humans. however, less is known about the capabilities of newer, larger models in these areas. recent work with newer models including llama 2 and gpt-3 on noun-noun compounds-whose meaning arises less straightforwardly from their parts than adjective-noun combination (hacken, 2016)shows that the llms do not generalize well in this case @xcite @xcite . the second dataset, which we refer to as con-text, shows that providing a context with appropriate detail is sufficient to determine the inference for typically-privative adjectives. participants first read a short 50-word context and then answer "in this setting, is an {adjective} {noun} a {noun}?" the dataset contains 56 expert-written contexts for 28 bigrams, with one privative-biased and one subsective-biased context for each bigram; an example is shown in appendix b.1. 6 bigrams are zero-frequency in c4 and a further 7 are lowfrequency (below median frequency in the total set). this dataset is much smaller due to the need for trained annotators to create the contexts. ## experiment 1: in-context meaning generalization typical llm evaluations assume a single correct answer for each question. thus, we begin with the smaller context dataset from @xcite where the contexts provided are sufficient to determine the inference. our evaluation focuses on the following two aspects: (1) whether the llm is sensitive to the provided context, and (2) whether the llm is able to choose the intended inference both for high-frequency bigrams it has presumably seen during pretraining and also for zero-frequency bigrams which we presume it has not. ## experiments 2 and 3: evaluating inferences without context the no-context dataset in @xcite asks the same inference question "is an {adjective} {noun} a {noun}?", but without providing any additional context to help determine the inference. this results in a wide distribution of human ratings for some (but not all) bigrams involving typically privative adjectives. for example, a counterfeit dollar bill is never judged to be a dollar bill. the distribution also widens for some bigrams with typically subsective adjectives, such as homemade cat. in this section, we propose three methods to investigate whether llms can match the distribution of ratings provided by humans in this context, since there is no longer a single correct answer. ## conclusion in this paper, we study whether llms can combine adjectives and nouns to yield noun membership inferences both with and without context, for highfrequency and zero-frequency (presumed novel) bigrams. we find that when the inference is determined by context, large, recent llms make the expected inferences, while smaller and/or older llms only sometimes do so. all llms, even smaller ones, behave similarly for zero-frequency bigrams as they do for high-frequency bigrams, suggesting that they do not rely on having seen them and their associated inference during pretraining. instead, they evaluate these combinations on-thefly and can generalize accordingly, as humans do. we investigate three methods to evaluate whether llms can extend this behavior to a setting where no context is provided, where they either need to fall within the human distribution of ratings or match the whole human distribution. while recent llms are able to fall within 1 sd of the human mean for up to 95% of our dataset, this is a very lenient metric. on our stricter metric of matching the human distribution (either using log-probabilities or via context generation), we find that these llms are good at capturing the simple distribution of bigrams like multicolored flower and counterfeit watch, but are only able to capture up to 75% of our total dataset. llms struggle particularly with bigrams with unusual inferences for their adjective, such as homemade cat, and with bigrams with high human variance. interestingly, however, llms are still equally able to capture distributions of novel bigrams in this setting. in sum, our generalization results are exciting because they show llms are generalizing beyond their training data even in this delicate, context-sensitive task, but there is still room for improvement on the task of capturing these inferences overall. further, we hope that the methods presented in this paper will inspire future work which moves beyond targeting single correct answers and begins to target human population distributions, from meaning and inferences to wider issues such as opinions and political positions.