CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

• URL: http://arxiv.org/abs/2310.15239v1
• Date: Mon, 23 Oct 2023 18:00:23 GMT
• Title: CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
• Authors: Mete Ismayilzada, Debjit Paul, Syrielle Montariol, Mor Geva, Antoine Bosselut
• Abstract summary: We present CRoW, a benchmark that evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks. 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.
• Score: 29.35269979211728
• License: http://creativecommons.org/licenses/by/4.0/
• 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 at https://github.com/mismayil/crow.

Related papers

• BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data [61.936320820180875]
  Large language models (LLMs) have become increasingly pivotal across various domains. BabelBench is an innovative benchmark framework that evaluates the proficiency of LLMs in managing multimodal multistructured data with code execution. Our experimental findings on BabelBench indicate that even cutting-edge models like ChatGPT 4 exhibit substantial room for improvement.
  arXiv  Detail & Related papers  (2024-10-01T15:11:24Z)
• Efficacy of Synthetic Data as a Benchmark [3.2968976262860408]
  We investigate the effectiveness of generating synthetic data through large language models (LLMs) Our experiments show that while synthetic data can effectively capture performance of various methods for simpler tasks, it falls short for more complex tasks like named entity recognition. We propose a new metric called the bias factor, which evaluates the biases introduced when the same LLM is used to both generate benchmarking data and to perform the tasks.
  arXiv  Detail & Related papers  (2024-09-18T13:20:23Z)
• D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning [99.33607114541861]
  We propose a new benchmark for offline RL that focuses on realistic simulations of robotic manipulation and locomotion environments. Our proposed benchmark covers state-based and image-based domains, and supports both offline RL and online fine-tuning evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:27:00Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions [114.67699010359637]
  We analyze a large-scale collection of real user queries to GPT. We find that tasks such as design'' and planning'' are prevalent in user interactions but are largely neglected or different from traditional NLP benchmarks.
  arXiv  Detail & Related papers  (2023-10-19T02:12:17Z)
• ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering [70.6359636116848]
  We propose a new large-scale dataset, ConvFinQA, to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations.
  arXiv  Detail & Related papers  (2022-10-07T23:48:50Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)
• Offline Equilibrium Finding [40.08360411502593]
  We aim to generalize Offline RL to a multi-agent or multiplayer-game setting. Very little research has been done in this area, as the progress is hindered by the lack of standardized datasets and meaningful benchmarks. Our two model-based algorithms -- OEF-PSRO and OEF-CFR -- are adaptations of the widely-used equilibrium finding algorithms Deep CFR and PSRO in the context of offline learning.
  arXiv  Detail & Related papers  (2022-07-12T03:41:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.