CR o W : Benchmarking Commonsense Reasoning in Real-World Tasks (2023)

Abstract: Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these datasets formulate commonsense reasoning challenges in artificial scenarios that are not reflective of the tasks which real-world NLP systems are designed to solve. In this work, we present CRoW, a manually-curated, multi-task benchmark that evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks. CRoW is constructed using a multi-stage data collection pipeline that rewrites examples from existing datasets using commonsense-violating perturbations. We use CRoW to study how NLP systems perform across different dimensions of commonsense knowledge, such as physical, temporal, and social reasoning. We find a significant performance gap when NLP systems are evaluated on CRoW compared to humans, showcasing that commonsense reasoning is far from being solved in real-world task settings. We make our dataset and leaderboard available to the research community.

## introduction commonsense reasoning is a long-standing challenge in artificial intelligence (ai) and natural language processing @xcite @xcite , resulting in a large number of datasets and benchmarks designed to evaluate how ai systems reason in commonsense scenarios described in natural language @xcite . recently, large language models, such as gpt-3 @xcite and palm @xcite , have demonstrated near-human performance on many of these benchmarks @xcite . however, these models can still be brittle in practical deployments, raising bob decided to volunteer because he wanted to eat the candies. bob decided to eat the candies because he wanted to volunteer. ok ✔ ✔ ✔ ✘ doesn't want figure 1 : an example from one of the tasks (dialogue) in our benchmark showcasing the difference between the evaluation of commonsense reasoning in an artificial and real-world setting. crowgrounds this evaluation in a real-world context that often requires the use of rich and implicit commonsense knowledge to solve a task. part of this issue stems from the practice that most commonsense datasets are designed to evaluate reasoning in artificial task settings that are not reflective of the real-world use cases in which nlp systems are deployed. in real-world settings, one almost never directly observes a test of commonsense knowledge in isolation. in this paper, we argue instead that commonsense reasoning benchmarks should evaluate commonsense reasoning in the tasks in which these abilities are required. the necessity of commonsense to solve realworld tasks has been extensively argued since the early stages of ai, notably by @xcite in the context of machine translation. however, despite these early arguments, only recently was there an attempt to construct a commonsense reasoning dataset for machine translation @xcite , an effort which concluded that the commonsense reasoning abilities of modern models were still in their infancy when applied in real nlp tasks. in this work, we build on these original ideas and introduce crow: a commonsense reasoning benchmark for real-world tasks, a multi-task benchmark containing high-quality datasets for six real-world nlp tasks: machine translation (mt), open-domain dialogue (dg), dialogue summarization (ds), intent detection (id), stance classification (sc), and safety detection (sd). inspired by winograd schemas @xcite , we build our benchmark by applying commonsensebased minimal perturbations on examples from existing datasets for each task. for each of these tasks, we crowdsource collections of potential target references for the task, each grounded to a particular commonsense violation with respect to the original context (see figure 1 for examples in dialogue response generation). we categorize these commonsense violations across six dimensionstemporal, causal, attribution, comparison, physical, and social -ensuring a diverse breakdown of commonsense reasoning types in crow. our empirical study across 13 state-of-the-art (sota) systems (including gpt-4) shows that crow is a challenging commonsense reasoning testbed, with the highest performing model scoring ∼18% lower than humans on individual examples and ∼37% lower on our more restrictive metric that evaluates situational robustness. consequently, we provide crow to the community as the first commonsense benchmark specifically formed to test commonsense knowledge and reasoning abilities in the same contexts as real-world deployments of nlp systems. the contributions of our work can be summarized as follows: commonsense reasoning benchmarks many benchmarks measuring the commonsense reasoning abilities of state-of-the-art models have been released in recent years. starting with the well-known winograd schema challenge (wsc; @xcite , these benchmarks have attempted to test the commonsense reasoning ability of models using different task formats, such as pronoun resolution @xcite @xcite , questionanswering @xcite @xcite @xcite , plausible inference @xcite @xcite @xcite and natural language generation @xcite . benchmarks have also been created to evaluate commonsense reasoning across different dimensions of commonsense knowledge, including social @xcite , physical @xcite @xcite , temporal @xcite and numerical reasoning @xcite . additionally, there exist comprehensive multi-task benchmarks that consist of several new or existing datasets for commonsense reasoning @xcite @xcite . for a thorough survey in this area, we refer readers to @xcite . in contrast to these benchmarks, where the underlying task formulation is centered around a task that is typically not grounded in a real-world setting, we construct crow to specifically focus on evaluating commonsense reasoning in real-world tasks for which nlp systems would be deployed. commonsense reasoning in real-world contexts a few recent works have explored the role of commonsense knowledge in real-world settings, such as open-ended response generation @xcite @xcite , machine translation @xcite and reading comprehension @xcite and have proposed new commonsense reasoning tasks and benchmarks. we build on top of these benchmarks and extend them to several other real-world nlp tasks, along with a general data collection methodology for commonsense knowledge annotation and winograd-style schema generation that can be applied to other tasks in the future. ## data collection our goal is to assess the ability of nlp systems to apply commonsense reasoning in real-world tasks. to this end, we define a general methodology and multi-stage data collection pipeline (figure 2 ) for generating evaluation examples that require commonsense reasoning in a given real-world task. in what follows, we outline our general data collection methodology, and describe each step in detail. ## crow crow consists of six real-world nlp tasks where commonsense reasoning ability is implicitly required to solve the task correctly. initially, to select tasks that could serve as good testbeds for crow, we followed davis (2023), and identified the following desiderata of tasks in the benchmark: (1) tasks should represent real-world applications of natural language technologies (e.g., machine translation), (2) tasks should involve rich commonsense knowledge use and reasoning, and (3) tasks should be easy for humans. our final benchmark contains ∼5k unique contexts with ∼500 unique contexts per task (on average) and ∼16k examples (i.e., context-target pairs) in total. ## experimental setup task formulation. all tasks in crow are treated as binary classification tasks. given a context, a model must predict whether a provided target is a suitable response for the corresponding real-world task. for instance, in machine translation, given an english sentence and a translated sentence in french, the model must predict whether the translation is valid or not. evaluation metrics. we evaluate models on crow using two scores: macro-f1 of predicting valid and invalid targets, and situational accuracy, a stringent metric that reports whether the model correctly identifies the validity (or invalidity) of all targets for a given context (similar to storks and chai, 2021's strict coherence score). a single mistake on any target results in a score of 0 for that context. we design this metric to account for the fact that robust commonsense reasoning would provide the model with a full situational understanding of the context. the crow score is computed as a macro-average of the task scores. models. we evaluate a series of language models that are diverse in terms of scale, training, and data: • llama @xcite , an open-source decoder-only model with various sizes (7b, 13b, 33b parameters) and palm-1-540b (chowdhery et al., 2022), a closed-source decoder-only model with 540b parameters. both models are pretrained using only a language modeling loss. human evaluation. we evaluate the human performance on each task of the benchmark using two expert annotators who evaluate 100 random samples from the task. our experts are nlp researchers from our lab who were not involved in the original data collection. as a result, they are more experienced, can clarify misunderstandings in the annotation guideline with us, and generally produce more careful annotations than crowd workers. following @xcite and @xcite , we intentionally allow evaluators to discuss and reach a final answer in cases of disagreement, which reduces variance and yields a robust upper bound for our task. in appendix d, we provide further details on the number of resolved disagreements, the human performance before and after the discussion, and the statistical significance of the 6 further results with varying temperature values are in appendix e. 7 more details on prompt templates are in appendix c. ## results instruction-tuning. models that were trained only with language modeling objectives (e.g., llama and palm) obtain lower scores compared to instruction-tuned models of similar size. for example, alpaca, which is an instruction-tuned version of llama-7b, achieves an average ∼10% improvement compared to llama-7b across most tasks for both metrics. also, smaller instructiontuned models can perform similarly or exceed the performance of much larger models (e.g., gpt-3.5 outperforms palm). finally, we find that stable-vicuna surprisingly performs worse than vicuna, suggesting that while instruction-tuning improves performance on crow, training with rlhf does not necessarily amplify the commonsense reasoning abilities required for these tasks. scale. when we compare the same model with different scales, we do not find a consistent benefit to increasing the size of the model, except on the safety detection task, where llama-33b achieves a 14.2% and 11.4% improvement score over llama 7b and 13b, respectively. multilinguality. most of these models are officially monolingual, though they may have been pretrained on some non-english data. since one of our testbed tasks centers on machine translation, we evaluate multilingual models on our benchmark. bloomz performs better than mt0 across most tasks. certain monolingual models outperform bloomz on translation tasks (i.e., those @xmath0, suggesting these models have seen multilingual data during their pretraining phase. ## analysis dimensions of commonsense knowledge. can models leverage explicit commonsense knowledge? when constructing crow, we annotate the implicit commonsense relationship required to understand whether a target is valid or invalid given a context. to test whether models can leverage this implicit commonsense relationship for making a correct prediction, we perform an oracle experiment where we augment the prompt with each example's annotated commonsense knowledge triplet. qualitative analysis. following up on the lower performance of gpt-4 with cot prompting compared to gpt-4, we qualitatively analyze the errors made by the model in the cot setting for poten- dialogue a: did you see may today? b: yes, but why does she have such a long face? a: i don't have the foggiest idea. b: i thought she'd be happy. knowledge getting a promotion implies being happy response a: yeah, especially since she got fired recently. gpt-4: not plausible ✔ gpt-4-cot step 1: identify the main topics in the dialogue. -seeing may -may's mood -may's expected happiness step 2: analyze the response. ## conclusion in this work, we propose crow, a multi-task commonsense reasoning benchmark consisting of six real-world tasks. to construct our benchmark, we design a data collection pipeline to systemati-cally crowdsource winograd-style schemas based on commonsense-violating minimal perturbations. our evaluation of recent large language models on our benchmark shows that the performance of state-of-the-art models still falls far below human performance with respect to commonsense reasoning in real-world contexts.