Characterizing Human and Zero-Shot GPT -3.5 Object-Similarity Judgments (2024)

Abstract: Recent advancements in large language models’ (LLMs) capabilities have yielded few-shot, human-comparable performance on a range of tasks. At the same time, researchers expend significant effort and resources gathering human annotations. At some point, LLMs may be able to perform some simple annotation tasks, but studies of LLM annotation accuracy and behavior are sparse. In this paper, we characterize OpenAI’s GPT-3.5’s judgment on a behavioral task for implicit object categorization. We characterize the embedding spaces of models trained on human vs. GPT responses and give similarities and differences between them, finding many similar dimensions. We also find that despite these similar dimensions, augmenting humans’ responses with GPT ones drives model divergence across the sizes of datasets tested.

## introduction large language models (llms) are capable of accomplishing a variety of language-oriented tasks in zero-or few-shot settings @xcite . examples include common natural-language understanding and processing (nlu/p) tasks such as sentiment analysis and classification @xcite , language translation @xcite , and named entity recognition @xcite ; but also applied domains such as text tagging @xcite , multimodal tagging @xcite , and text sample augmentation @xcite . current llm performance indicates we may be able to use pre-trained high-resource llms to augment human annotations for tasks where data is sparse or compute resources are low @xcite . however, we do not currently know for which domains it is appropriate to augment human data with llm-generated responses. this uncertainty stems from a poor understanding of how llm and human annotation responses systematically differ. thus, characterizing the ways in which world knowledge manifests itself in the generations of llms is crucial for incorporating llms into annotation workflows. 1 the domain of object-similarity judgment is a useful base-case for exploring the similarities and substitutability of llm for human responses. on a human level, object-similarity judgment informs how we interact with objects @xcite , organize our world @xcite and acquire new concepts from a young age @xcite . meanwhile, many corpusbased computational models, including deep transformer models that leverage corpora such as chat-gpt, leverage lexical co-occurrence relations to derive semantic meaning (i.e. the distributional hypothesis). despite differences in process, these models' representations display correspondences with human judgment @xcite . in this paper, we collect gpt-3.5 responses to an object similarity task introduced by @xcite . we reformat their image-based paradigm as a chat-completion task for gpt. 2 like hebart et al., we also train a sparse embedding model that can predict object-similarity judgments. we annotate the dimensions of the embedding model to provide an interpretable characterization of the reasoning behind such judgments. finally, we compare the gpt-and human-derived characterizations and embeddings. we also simulate the effects of gpt response replacement and augmentation. to do this, we train models on different mixtures and proportions of human and gpt responses, then compare their embedding spaces to baseline human-derived ones. ## methodology the odd-one-out (ooo) task to obtain object-similarity responses from gpt, we used the odd-one-out (ooo) task, wherein participants indicate the least similar amongst three objects. for example, we might ask, "which of these concepts is the odd one out: apple, banana, car?" and expect factors such as edibility to affect the response. the ooo task is well-established in the field of psychology for eliciting concept-relational preferences @xcite . hebart et al. (2020) used an image-based ooo task to collect millions of object-similarity judgements. they did this in two rounds, first collecting 1.46m responses @xcite , then creating a larger, 5m response dataset @xcite . 3 we used these two datasets to create two disjoint ooo response sets of equal size (1.46m). we refer to the first of these datasets as the full human dataset and the second as the baseline dataset. ## conclusions our work illustrates gpt's judgment in an oddone-out similarity task, provides 39 judgmentcharacterizing dimensions with human annotations, and compares those dimensions with those derived from a human-only model. notably, many gpt (and human) dimensions have similar, shared-wordor-synonym labelling, such as food-related (food-related/eating-related/kitchen-related) and animalrelated/organic (animal-related/organic). we compared the labelled gpt and human dimensions, and we found that over half of the labelled gpt dimensions had correlations above 0.5 with a similarly labelled human one (for individual results, see figure 2 or figure 12 ). however, while using gpt responses did produce many characterizing dimensions similar to those derived from human responses, substituting in gpt responses still resulted in worse approximations of human decisionmaking under rsa, as demonstrated by figure 4 . some of this is likely attributable to modality differences between the image and text questions, as some of the dimensions least captured by the model are color-oriented, such as "wood/brownish", "red", and "colorful", as shown in figure 3 and appendix i. surprisingly, even when we used relatively little human data, adding gpt responses did not improve the trained model's rsa score. as shown by figure 4 , there must be a point below which this improvement appears, as the full gpt rsa score is above 0.4, a randomly initialized embedding has an rsa score of 0, and there's very little information present in, e.g., one triplet. however, that point was below the lowest proportion we tested, as shown by gaps between the rsa scores of the partial human models and the gpt-augmented mixed human-gpt models in figure 4 . this ostensibly contradicts previous studies showing llms having human-comparable performance on a wide variety of tasks, but human-level performance is different than human behavior. partial human rsa scores largely saturated by the time 0.375x the full human dataset was used, indicating that for rsa purposes, the sizes of the partial human datasets we considered may have been larger than needed. nonetheless, it seems misguided to augment with gpt responses unless human data is considerably scarcer than we tested, as the mixed human-gpt datasets have considerably worse rsa scores than those observed on much smaller partial human datasets. were our task extremely low-resource, the 0.42 rsa score achieved by the full gpt model might be useful, however. encouragingly, when swapping human responses with those from gpt, the rsa scores appeared fairly robust to replacement, as shown by the full human and partial human models' rsa scores in figure 4 . when we replaced 25% of the data (0.75 on the human-data-portion x-axis) the rsa score dropped by less than 10%. there was at most a ∼60% reduction when 100% was replaced (0 on the human-data-portion x-axis). this is important to consider for future crowdsourced oddone-out experiments, such as the hebart dataset, because many crowdworkers have begun using gpt for their tasks. in conclusion, our work characterizes gpt object-similarity judgments, enhancing our understanding of how llms and humans behave similarly or differently. notably, despite a modality difference, gpt responses produced embeddings with labels mirroring or closely resembling those from human responses. our findings also indicate utility in using llm completions for extremely low-resource environments as a proxy for human judgment. however, these findings suggest little benefit from augmenting human responses for any sizeable number, especially when crowdsourcing human data is feasible. our findings also warrant caution when otherwise human-looking gpt responses might become part of collected data, but offer hope for the odd-one-out task, as the embeddings proved fairly robust to lower levels of response replacement.