A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022)

Abstract: Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand the extent to which such models learn commonsense knowledge — a critical component of many NLP applications. We conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained LMs, where we: (i) carefully control for the LMs’ ability to exploit potential surface cues and annotation artefacts, and (ii) account for variations in performance that arise from factors that are not related to commonsense knowledge. Our findings highlight the limitations of pre-trained LMs in acquiring commonsense knowledge without task-specific supervision; furthermore, using larger models or few-shot evaluation is insufficient to achieve human-level commonsense performance.

## introduction common sense -the implicit knowledge about everyday situation that is shared by humans -is an important prerequisite for developing generalpurpose intelligent systems @xcite @xcite . intriguingly, recent large language models (lms, @xcite @xcite have achieved remarkable performance at various common sense benchmarks (e.g., @xcite @xcite , even when they are evaluated in a zero-shot or few-shot fashion, without explicit commonsense supervision. we revisit this apparent success, and conduct a rigorous study to better understand the extent to which such pre-trained lms are able to capture commonsense knowledge. in this work, we focus on zero-and fewshot evaluations of pre-trained lms without commonsense-specific fine-tuning for two reasons: first, we aim to examine if a pre-trained lm is able to acquire general commonsense knowledge. as pre-trained lms constitute a foundational building block of nlp today, any deficiencies in their commonsense understanding can thus adversely manifest in downstream applications @xcite . fine-tuning the lm would make it hard to disentangle how much of the commonsense knowledge is acquired by the underlying lm, as opposed to the task-specific supervision from a benchmark @xcite . second, humanannotated commonsense datasets are expensive to collect due to the vast, diverse, and growing nature of commonsense knowledge @xcite . concretely, our work differs from prior work on commonsense evaluation of lms @xcite by way of a more rigorous evaluation, in which we: (i) carefully control for the lm's ability to exploit potential surface cues and annotation artefacts to predict the answer, without reasoning over the context. we further (ii) account for the variations in factors influencing the lm's performance, which arise from certain evaluation design choices -independently of common-sense knowledge in the models. we systematically conduct this study on four commonsense benchmarks, six model sizes (up to a very large lm with 280b parameters), and multiple evaluation settings (e.g., different score functions and prompt format). we begin with our first question: when evaluating a large lm in a zero-shot setting, how does its zero-shot performance compare to a strong baseline ( §3)? controlling for the lm's ability to guess the correct answer, without even looking at the question @xcite , answer-only baseline, top of fig. 1 ), we find that, despite the lm's strong zero-shot performance, the answer-only baseline can nevertheless perform surprisingly well on some benchmarks. despite the clear importance of comparing with answer-only baselines as shown in figure 2 , these comparisons are absent from recent work on large lms @xcite @xcite . furthermore, increasing model size alone is unlikely to bridge the gap with human performance in the near future: our analysis of scaling behavior suggests that much larger dense lms (with 100t to 10 18 parameters -which are infeasibly large at present) are needed to achieve human performance for 3 out of 4 benchmarks. does familiarizing the lm with the task format using a few-shot evaluation setting substantially improve performance ( §4)? we find that the fewshot evaluation (using up to 64 examples) does not substantially improve the lms' performance for most tasks except social iqa. moreover, using the few-shot/in-context demonstration setting fails to bridge the gap between the lm and current sota. finally, we ask: to what extent does the model's zero-shot performance vary depending on certain evaluation design choices, such as the format of the prompt or the score function ( §5)? we find that these design choices -though they have little to do with common sense -can result in large fluctuations in @xcite . this finding challenges the notion that large lms are largely able to work well out-of-the-box with minimal taskspecific tuning. based on these findings, we emphasize the need to carefully select such design choices, explicitly state them to enable fair comparison with prior work, and quantify the robustness of the observed results across different design choices. all in all, our findings suggest that acquiring human-level commonsense knowledge, without relying on surface cues or task-specific supervision, remains beyond the reach of current large lms. given the marginal improvements from increasing model size, we conjecture that other techniques, such as explicit commonsense supervision, multimodal grounding, or physical embodiment @xcite , are promising ways forward. ## experimental setting we begin by outlining our experimental setup, and describe the benchmarks, model, baselines, and other relevant experimental settings. ## zero-shot performance in fig. 2 , we report the zero-shot performance of our pre-trained lm (with 280b parameters, §2.2) on the four commonsense benchmarks, alongside: (i) the random and answer-only baselines, and (ii) the current state-of-the-art (sota) result. the sota results are achieved by the uni-corn @xcite model with 11b parameters, which is pre-trained on 6 existing commonsense datasets @xcite @xcite @xcite . zero-shot performance. at first glance, we observe strong zero-shot results, outperforming the random baseline in all benchmarks (compare "rand" and "zs" in fig. 2 ). however, the gap between the stronger answer-only baseline and the zero-shot result is smaller for all benchmarks (compare "answer" and "zs"): whereas this gap is still sizable for hellaswag and winogrande (>20), it is much smaller for social iqa and piqa. finally, in all cases, there is still a large gap between the sota and zero-shot performance (>10); this gap is largest for winogrande and social iqa, suggesting that social and physical commonsense is challenging for pre-trained lms -even a large one with 280b parameters -without task-specific supervision. foot_3 ## few-shot performance recent work has shown that large lms can perform surprisingly well at various tasks in a fewshot fashion @xcite . under this setup, the model is provided with n examples of the downstream task, which are then appended to the prefix. concretely, for the four commonsense benchmarks, we append n examples that include the question and the correct answer; these examples -which are randomly sampled from the training split of each benchmark -appear before the evaluated question, as shown in fig. 1 . this few-shot formulation is appealing as it relies only on a small number of task-specific examples to get the lm accustomed to the task, without any fine-tuning. to what extent can we improve the model performance on commonsense benchmarks, by shifting from the zero-shot to the few-shot evaluation protocol? 6 in fig. 5 , we compare the performance of gopher under different evaluation protocols: (i) zeroshot and (ii) few-shot (n) where we use @xmath0, 10, 64} examples. we run the few-shot experiments between 5 and 10 times -sampling different examples each time -and report the average performance. the variance across runs is very small and is shown as the error bar in fig. 5 . 7 interestingly, model performance with few-shot (1) is sometimes worse than the zero-shot model, but the few-shot (10) and (64) models outperform their zero-shot counterpart (albeit sometimes by small margins). on hellaswag and piqa, we do not observe substantial improvement from few-shot evaluation compared to the zero-shot baseline (less than 2%). 8 while few-shot evaluation does not help much for most datasets, the only exception is social iqa, where the few-shot (64) model outperforms the zero-shot model by @xmath1. we attribute this to the less natural text of social iqa; foot_8 hence adding task-specific examples provides information about what is expected of the task. overall, we observe that the usefulness of the few-shot setting is benchmark dependent. moreover, using task-specific examples in a few-shot setting does not bridge the gap to sota or human performance for any of the benchmarks. ## robustness of reported results different evaluation design choices -such as the format of the prompt or the choice of score functions -can impact the lm's zero-shot performance, and crucially result in different conclusions about a model's commonsense understanding ability. moreover, the lack of a standardized zeroshot lm evaluation protocol makes direct comparisons between papers difficult @xcite . to what extent can we attribute variance in the reported results to these evaluation design choices -even though they have little to do with commonsense knowledge? model. quantifying the robustness of the reported results necessitates scoring a large number of examples under different evaluation design choices, which is infeasible to do with the largest (280b-parameter) model that has a slow inference speed. hence, we conduct the following experiments using the 7b-parameter model, which is still ∼5 times larger than gpt2 @xcite . ## related work while recent work evaluates lms against commonsense benchmarks in a zero-and few-shot fashion, they do not examine the extent to which model performance can be attributed to superficial cues or annotation artefacts in a given dataset (e.g., through strong baselines), nor do they quantify how robust the model performance is under different evaluation design choices. @xcite ; @xcite investigate the existence of dataset bias in commonsense co-reference resolution benchmarks @xcite and swag @xcite ; here we conduct a more comprehensive investigation on four diverse commonsense benchmarks. another line of work probe for commonsense knowledge in lms through knowledge base completion @xcite or manually-designed probing tasks @xcite . @xcite evaluate pre-trained lms against commonsense benchmarks and propose a new dataset requiring multi-hop reasoning. in contrast, we focus on zeroand few-shot evaluation of commonsense understanding using the existing benchmarks. ## conclusion we conduct a systematic and rigorous study of large lm performance on a diverse set of commonsense benchmarks, in a zero-shot and few-shot fashion. while pre-trained lms can seemingly achieve a good zero-shot performance on these benchmarks, these results can be partially attributed to the lm's ability to exploit potential surface cues and annotation artefacts to guess the correct answer, without reasoning over the provided context. we further observed that substantially increasing model size yields rather small improvements on most commonsense benchmarks: based on the scaling plots, achieving human-level performance requires much larger model sizes than what is currently feasible. in addition, model performance can be highly sensitive to certain evaluation design choices. overall, our findings offer valuable insights and best practices for rigorously evaluating large lms.