Papers with Commonsense reasoning
LaMI: Augmenting Large Language Models via Late Multi-Image Fusion (2026.acl-short)
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| Challenge: | Large Language Models lack visual grounding on visual reasoning, despite training on text alone. |
| Approach: | They propose a late multi-image fusion method that augments LLMs with test-time visual signals. |
| Outcome: | Using a late multi-image fusion method, the proposed model outperforms LLMs on visual reasoning and matches VLMs in vision-based tasks. |
Common Sense or Ableism? Rethinking Commonsense Reasoning Through the Lens of Disability (2026.eacl-short)
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| Challenge: | a recent study finds that commonsense reasoning is not always universal and can leave disabled people behind . a case study of disabled people with long COVID shows that common sense is not universal . |
| Approach: | They investigate how datasets and models deal with disability in commonsense reasoning . they use annotations from disabled and non-disabled persons for ableism . |
| Outcome: | The proposed datasets have low sensitivity to human-detected ableism but still detect 5 to 25% of entries as ableist. |
LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning (2026.acl-short)
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| Challenge: | LOGICAL-COMMONSENSEQA benchmarks evaluate commonsense reasoning as logical composition over pairs of atomic statements . commonsensible reasoning is central to human cognition and a long-standing challenge in artificial intelligence and natural language understanding. |
| Approach: | They propose a benchmark that reframes commonsense reasoning as logical composition over pairs of atomic statements using plausibility-level operators. |
| Outcome: | LOGICAL-COMMONSENSEQA exposes fundamental reasoning limitations and provides a framework for advancing compositional commonsense reasoning. |
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences (2021.findings-acl)
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| Challenge: | Recent advances in pretrained language models have shown promising results on commonsense reasoning benchmark datasets. |
| Approach: | They propose a commonsense reasoning benchmark dataset with 4k sentence pairs . they propose 'gamified' model-in-the-loop setup to incentivize challenging samples . |
| Outcome: | The proposed benchmarks show that the proposed model achieves 71% standard accuracy and 51% pairwise accuracy, well below human performance. |
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (N19-1)
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| Challenge: | Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases. |
| Approach: | They propose two unsupervised models based on the Deep Structured Semantic Models framework to tackle two commonsense reasoning tasks: Winograd Schema Challenge (WSC) and Pronoun Disambiguation (PDP). |
| Outcome: | The proposed models capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches. |
Neural-Symbolic Commonsense Reasoner with Relation Predictors (2021.acl-short)
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| Challenge: | Existing models for commonsense reasoning are limited by their limited set of facts, rendering them unfit for reasoning over new unseen situations and events. |
| Approach: | They propose a neural-symbolic reasoner which can combine commonsense facts with large-scale dynamic CKGs to draw conclusions about ordinary situations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the task of link prediction on CKGs. |
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models (2022.findings-naacl)
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| Challenge: | Existing approaches to enhance pre-trained language models (PTMs) with a knowledge-aware graph neural network (GNN) encoder that models a commonsense knowledge graph (CSKG) can't explain how external knowledge resources improve the reasoning capacity of PTMs. |
| Approach: | They propose to use relation features from CSKGs to enhance the reasoning capacity of pre-trained language models (PTMs) by encoding a commonsense knowledge graph (CSKG) |
| Outcome: | The proposed approach reduces the parameters for encoding CSKGs and improves on five benchmarks. |
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
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| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
Leveraging Knowledge in Multilingual Commonsense Reasoning (2022.findings-acl)
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| Challenge: | Commonsense reasoning is a language-agnostic process, but most comprehensive knowledge sources are limited to a small number of languages, especially English. |
| Approach: | They propose to use English as a pivot language to integrate commonsense reasoning into models using a translate-retrieve-translate strategy. |
| Outcome: | The proposed model outperforms the state-of-the-art on the XCSR benchmarks. |
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (D19-1)
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| Challenge: | empowering machines with the ability to perform commonsense reasoning has been seen as the bottleneck of artificial general intelligence . |
| Approach: | They propose a textual inference framework that uses external commonsense knowledge graphs to answer commonsensical questions. |
| Outcome: | The proposed framework is based on graph convolutional networks and LSTMs with a hierarchical path-based attention mechanism. |
It’s All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning (2021.findings-acl)
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| Challenge: | gilbert et al.: commonsense reasoning is a key problem in natural language processing but its capabilities are still unstudied. gilland eetal.: a new approach to commonsensible reasoning is needed to solve the problem. |
| Approach: | They propose a method which trains a linear classifier with weights of multi-head attention as features and a multilingual Winograd Schema corpus to measure cross-lingual generalization ability. |
| Outcome: | The proposed approach performs competitively with recent approaches even when applied to other languages in a zero-shot manner. |
Commonsense Reasoning in Arab Culture (2025.acl-long)
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Abdelrahman Sadallah, Junior Cedric Tonga, Khalid Almubarak, Saeed Almheiri, Farah Atif, Chatrine Qwaider, Karima Kadaoui, Sara Shatnawi, Yaser Alesh, Fajri Koto
| Challenge: | Existing studies on commonsense reasoning in Arabic have relied on machine translations that lack cultural depth and introduce anglocentric biases. |
| Approach: | They propose a commonsense reasoning dataset in Arabic that covers 13 Arab countries. |
| Outcome: | The proposed dataset covers 13 countries across the Gulf, Levant, North Africa, and the Nile Valley. |
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering (2020.coling-main)
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| Challenge: | Identifying the implicit causes and effects of a social context is the driving capability of commonsense reasoning. |
| Approach: | They propose a conditional seq2seq-based mixture model which generates context-dependent clauses for commonsense reasoning. |
| Outcome: | The proposed model improves on the current state-of-the-art models by +5.2% over existing models. |
Towards Quantifying Commonsense Reasoning with Mechanistic Insights (2025.naacl-long)
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| Challenge: | Recent studies have evaluated commonsense reasoning abilities using text-based tasks. |
| Approach: | They propose to capture commonsense knowledge in a graphical representation of 37 daily human activities in graphical form and frame them to frame commonsensical queries. |
| Outcome: | The proposed model can frame an enormous number of commonsense queries ( 10 17) and perform rigorous evaluations of common sense reasoning in LLMs. |
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions (2023.findings-acl)
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| Challenge: | Existing benchmarks focus on a single reasoning type and ask human annotators to write candidate statements related to the particular type of commonsense. |
| Approach: | They propose a new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs. |
| Outcome: | The proposed dataset is challenging to previous machine reading models and large language models with a significant 20%performance gap compared to human experts. |
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning (2023.acl-long)
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| Challenge: | HKUST-KnowComp proposes a framework for commonsense reasoning that can be used to conceptualize commonsence knowledge bases at scale. |
| Approach: | They propose a framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale. |
| Outcome: | The proposed framework achieves state-of-the-art on two conceptualization tasks and the acquired abstract commonsense knowledge significantly improves commonsence inference modeling. |
Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd (2025.findings-emnlp)
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| Challenge: | Multimodal UNcommonsense (MUN) is a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations. |
| Approach: | They propose a retrieval-based in-context learning framework that transfers reasoning capabilities from larger models to smaller ones without additional training. |
| Outcome: | The proposed method improves on baseline ICL methods by 8.3% over previous methods. |