Challenge: Recent work has found that large-scale language models lack commonsense reasoning ability . a dataset evaluating large-level language models is needed to evaluate their understanding of feasibility .
Approach: They propose a question-answering dataset that tests understanding of feasibility . they propose to use commonsense reasoning to reason about when an action is feasible .
Outcome: The proposed dataset shows that state-of-the-art models struggle to answer feasibility questions correctly.

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ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)

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Challenge: acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) .
Approach: They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions.
Outcome: The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets.
Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models (2026.acl-short)

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Challenge: Scientific feasibility assessment asks whether a claim aligns with established knowledge and whether experimental evidence could support or refute it.
Approach: They frame scientific feasibility assessment as a diagnostic reasoning task . given a hypothesis, a model predicts feasible or infeasible and justifies its decision . they evaluate large language models under controlled knowledge conditions .
Outcome: The results show that providing outcome evidence is more reliable than providing experiment descriptions.
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)

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Challenge: In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge.
Approach: This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning.
Outcome: This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias).
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures (2024.acl-long)

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Challenge: Existing commonsense evaluations are often posed as multiple-choice questions, allowing models to exploit systematic biases.
Approach: They propose a generative task that evaluates common sense via multiple open-ended generations and a method that strongly correlates with human judgments.
Outcome: The proposed method outperforms strong language model baselines on a dataset of human and machine common sense.
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)

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Challenge: a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets.
Approach: This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction .
Outcome: This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction .
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models (2024.emnlp-main)

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Challenge: LogicAsker examines and improves the reasoning abilities of large language models such as ChatGPT and GPT-4.
Approach: They propose a set of atomic reasoning skills grounded in propositional and predicate logic to examine and improve the reasoning abilities of large language models such as ChatGPT and GPT-4.
Outcome: The proposed approach improves reasoning abilities in large language models such as ChatGPT and GPT-4 by up to 5%.
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios (2022.coling-1)

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Challenge: Current studies in natural language processing do not focus on situated commonsense reasoning under multiple possible scenarios.
Approach: They frame a scenario task by asking multiple questions with the same set of possible endings as candidate answers, given a short story text.
Outcome: The proposed dataset shows that even strong pretrained models struggle to answer the questions consistently, highlighting that the highest accuracy in an unsupervised setting (60.2%) is far behind human accuracy (92.5%).
Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs? (2024.lrec-main)

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Challenge: Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) however, there are concerns about whether MCQ can truly measure LLM’s capabilities.
Approach: They propose to use multiple choice questions to evaluate large language models (LLMs) to assess their capabilities.
Outcome: The proposed methods show that MCQs are less reliable than LFGQs in terms of expected calibration error.
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)

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Challenge: Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing.
Approach: They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs.
Outcome: The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge.
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge (2021.findings-acl)

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Challenge: a riddle-style commonsense questions require complex commonsensense reasoning and figurative language skills . there is currently no dataset aimed at testing these abilities . authors propose a new multiple-choice question answering task .
Approach: They propose a new multiple-choice question answering task that uses a large dataset for riddlestyle commonsense questions.
Outcome: The proposed task comes with the first large dataset for answering riddlestyle commonsense questions.

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