Papers with commonsense reasoning tasks
A Hybrid Neural Network Model for Commonsense Reasoning (D19-60)
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| Challenge: | a hybrid neural network (HNN) model for commonsense reasoning is proposed . it combines language models and semantic similarity models to achieve new state-of-the-art results . |
| Approach: | They propose a hybrid neural network model for commonsense reasoning . it combines a masked language model and a semantic similarity model . |
| Outcome: | The proposed model outperforms the WNLI, WSC and PDP60 benchmarks on three commonsense reasoning tasks. |
Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning (2025.findings-acl)
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| Challenge: | Existing prompting methods struggle with complex tasks and reasoning stability, limiting their practical deployment. |
| Approach: | They propose a framework that adaptively balances reasoning accuracy and computational efficiency by employing a lightweight Derailer mechanism to assess reasoning stability and selectively triggers an advanced Rerailer verification process only when necessary. |
| Outcome: | The proposed framework achieves significant accuracy improvements (8-11%) while maintaining 2-3 times better efficiency than existing verification methods. |
Answer-level Calibration for Free-form Multiple Choice Question Answering (2022.acl-long)
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| Challenge: | Pre-trained language models exhibit few-shot and zero-shot learning capability on tasks such as commonsense reasoning. |
| Approach: | They propose to model context-independent biases in terms of the probability of a choice without the context and to remove it using an unsupervised estimate of similarity with the full context. |
| Outcome: | The proposed model improves over baselines on commonsense reasoning tasks. |
The Unintended Trade-off of AI Alignment: Balancing Hallucination Mitigation and Safety in LLMs (2026.findings-eacl)
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| Challenge: | Hallucination in large language models has been studied, but a side effect remains unrecognized . a new study examines the trade-off between truthfulness and safety alignment . |
| Approach: | They propose a method that disentangles hallucination from hallucinian features using sparse autoencoders. |
| Outcome: | The proposed method preserves refusal behavior and task utility while maintaining safety alignment. |
Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from extensive world knowledge acquired through extensive pretraining. |
| Approach: | They propose a method to generate knowledge explanations and to automatically assign labels based on the probability of correct answers. |
| Outcome: | The proposed method outperforms baselines on four widely-used commonsense reasoning benchmarks and shows that it can generate high quality knowledge leading to correct answers. |
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Existing studies focus on acquiring relevant knowledge by retrieving external knowledge bases and fine-tuning pre-trained models. |
| Approach: | They propose a two-stage prompt-based unsupervised commonsense question answering framework that leverages implicit knowledge stored in PrLMs to generate knowledge for questions with unlimited types and possible candidate answers independent of specified choices. |
| Outcome: | The proposed framework significantly improves the reasoning ability of language models in unsupervised settings. |
On Curriculum Learning for Commonsense Reasoning (2022.naacl-main)
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| Challenge: | Recent research suggests that data order can have a significant impact on the performance of finetuned models for natural language understanding. |
| Approach: | They use paced curriculum learning to rank data and sample training mini-batches with increasing levels of difficulty during finetuning. |
| Outcome: | The proposed model improves performance for socialIQA, CosmosQA, CODAH, HellaSwag, WinoGrande in both tuning settings. |
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. |
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. |
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)
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| Challenge: | Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks. |
| Approach: | They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs. |
| Outcome: | The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations. |
A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning (2024.lrec-main)
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| Challenge: | Existing models that provide evidence for commonsense reasoning tasks have limitations . evidence is often interpreted in ways that are not directly available in the input. |
| Approach: | They propose a trusted multi-view evidential fusion framework that assesses the confidence of evidence and combines different views in a trustworthy manner. |
| Outcome: | The proposed framework can reason with multi-view evidence and compete with state-of-the-art models. |
SafeText: A Benchmark for Exploring Physical Safety in Language Models (2022.emnlp-main)
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Sharon Levy, Emily Allaway, Melanie Subbiah, Lydia Chilton, Desmond Patton, Kathleen McKeown, William Yang Wang
| Challenge: | Existing models that generate unsafe text are susceptible to the dangers of unsafe text generation and are deemed unsafe. |
| Approach: | They use a dataset to empirically study commonsense physical safety across various models for text generation and reasoning tasks. |
| Outcome: | The proposed model can generate unsafe text and reject it, but the different harms that can occur do not receive equal attention, which may consequently downplay certain harms. |
Native Hybrid Attention for Efficient Sequence Modeling (2026.acl-long)
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| Challenge: | Experimental results show that NHA surpasses Transformers on recall-intensive tasks. |
| Approach: | They propose a hybrid architecture of linear and full attention that integrates both into a unified layer design. |
| Outcome: | The proposed architecture surpasses Transformers and other hybrid baselines on recall-intensive and commonsense reasoning tasks. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
Knowledge Rumination for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have shown that pre-trained language models lack the capacity to handle knowledge-intensive tasks alone. |
| Approach: | They propose a new paradigm to help pre-trained language models utilize latent knowledge without retrieving it from external corpus. |
| Outcome: | The proposed paradigm can be applied to pre-trained language models without retrieving external knowledge from the corpus. |
EENLP: Cross-lingual Eastern European NLP Index (2022.lrec-1)
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Alexey Tikhonov, Alex Malkhasov, Andrey Manoshin, George-Andrei Dima, Réka Cserháti, Md.Sadek Hossain Asif, Matt Sárdi
| Challenge: | Existing NLP resources for Eastern European languages are sparse. |
| Approach: | They propose to use existing Eastern European language resources to build cross-lingual datasets for five different semantic tasks to support commonsense reasoning. |
| Outcome: | The proposed model trains on 104 languages and shows impressive results on text analysis tasks. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
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Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach (2022.emnlp-main)
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| Challenge: | Existing methods for retrieving encyclopedic knowledge lack a large corpus and effective commonsense retriever. |
| Approach: | They propose a framework for retrieval-augmented commonsense reasoning with a large commonsensense corpus and a commonseense retriever. |
| Outcome: | The proposed framework outperforms existing methods on commonsense reasoning tasks. |
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty (2025.findings-naacl)
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| Challenge: | despite advances in large language models, they still produce false but incorrect responses. |
| Approach: | They propose a new benchmark for large language models that requires more than two unambiguous answers . they also assess 5 different uncertainty quantification methods in the presence of data uncertainty. |
| Outcome: | The proposed method fails in multi-answer question answering tasks compared to single-answered questions . entropy- and consistency-based methods effectively estimate model uncertainty, the authors show . |
Learning Contextualized Knowledge Structures for Commonsense Reasoning (2021.findings-acl)
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Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
| Challenge: | Recent knowledge graph (KG) augmented models have achieved notable success on commonsense reasoning tasks. |
| Approach: | They propose a KG-augmented model that contextualizes extracted and generated knowledge by reasoning over both within a single graph structure. |
| Outcome: | The proposed model outperforms existing models on four commonsense reasoning benchmarks and a user study on edge validness and helpfulness. |
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning (2020.acl-main)
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| Challenge: | Existing methods for solving common NLP tasks rely on fine-tuning of pre-trained transformer models. |
| Approach: | They propose a scoring method that casts a plausibility ranking task in full-text format without fine-tuning . they use masked language modeling head tuned during pre-training phase to exploit this method . |
| Outcome: | The proposed method produces strong baselines comparable to supervised approaches. |
Verifiable, Debuggable, and Repairable Commonsense Logical Reasoning via LLM-based Theory Resolution (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to substantial interest in their application to commonsense reasoning tasks. |
| Approach: | They propose a logical reasoning framework that integrates commonsense knowledge with a verifiable logical framework that mitigates hallucinations and facilitates debugging. |
| Outcome: | The proposed framework improves on three language-based reasoning tasks and improves accuracy and reasoning correctness. |
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization. |
| Approach: | They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question. |
| Outcome: | The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks. |
Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs (2024.acl-long)
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| Challenge: | Large language models (LLMs) have impressive human-like performance across various reasoning tasks, but their mastery of underlying inferential rules falls short of human capabilities. |
| Approach: | They propose a logic scaffolding inferential rule generation framework to construct an infer- ential rule base, ULogic, comprising both primitive and compositional rules across five domains. |
| Outcome: | The proposed model improves the ability to generate accurate, complex and abstract conclusions and premises and improves various commonsense reasoning tasks. |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
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| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
Iterative Structured Knowledge Distillation: Optimizing Language Models Through Layer-by-Layer Distillation (2025.coling-main)
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| Challenge: | Structured pruning and knowledge distillation are often not efficient and require a fixed architecture, limiting flexibility. |
| Approach: | They propose a method which integrates knowledge distillation and structured pruning by replacing transformer blocks with smaller, efficient versions during training. |
| Outcome: | The proposed method outperforms L1 pruning and maintains four-fifths of performance on language modeling and commonsense reasoning tasks. |
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization (2024.findings-emnlp)
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| Challenge: | Low-Rank Adaptation (LoRA) improves training efficiency by updating only a small portion of the weights in Large Language Models. |
| Approach: | They propose a rotation-aware scheme to fine-tune rotated outlier-free LLMs for effective weight-activation quantization. |
| Outcome: | The proposed method improves low-bit LoRA convergence and post-training quantization robustness. |
Social IQa: Commonsense Reasoning about Social Interactions (D19-1)
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| Challenge: | Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations. |
| Approach: | They propose a crowdsourcing framework that collects commonsense questions along with correct and incorrect answers about social interactions. |
| Outcome: | The proposed framework mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. |
Attention Is (not) All You Need for Commonsense Reasoning (P19-1)
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| Challenge: | Recent language models such as word2vec have produced impressive results on various tasks such as question-answering and natural language inference. |
| Approach: | They propose a simple re-implementation of BERT for commonsense reasoning . they propose to use attention-guided reasoning to solve the Pronoun Disambiguation Problem . |
| Outcome: | The proposed model outperforms the state-of-the-art on several language understanding benchmarks while outperforming the existing models by a margin. |
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024.findings-emnlp)
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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao
| Challenge: | Large language models (LLMs) often exhibit poor performance on knowledge-intensive tasks, such as commonsense reasoning. |
| Approach: | They propose a method to elicit, filter and integrate knowledge in large language models (LINKED) they propose 'reward model' to filter out noisy knowledge and 'take marginal consistent reasoning module' |
| Outcome: | The proposed method outperforms SOTA baselines on two commonsense reasoning tasks. |
Contrastive Self-Supervised Learning for Commonsense Reasoning (2020.acl-main)
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| Challenge: | Existing methods for commonsense reasoning are limited by current methods . empirical results show that our method alleviates the limitation of current supervised approaches . |
| Approach: | They propose a self-supervised method to solve pronoun disambiguation problems . they leverage a mutual exclusive loss regularized by a contrastive margin to achieve commonsense reasoning . |
| Outcome: | The proposed method performs well on many NLP benchmarks. |
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)
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| Challenge: | Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules. |
| Approach: | They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism. |
| Outcome: | Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets. |
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)
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| Challenge: | Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses. |
| Approach: | They propose a new method that enables LLMs to self-rank their responses without additional resources. |
| Outcome: | The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods. |
SpiralThinker: Latent Reasoning through an Iterative Process with Text–Latent Interleaving (2026.findings-acl)
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| Challenge: | Existing latent reasoning methods lack mechanisms to ensure stable reasoning dynamics in latent space and a systematic way to interleave implicit and explicit reasoning. |
| Approach: | They propose a framework that performs iterative updates over latent representations while enabling interleaved reasoning across latent and textual steps. |
| Outcome: | SpiralThinker achieves state-of-the-art among latent reasoning baselines. |
ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering (2026.acl-long)
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| Challenge: | Recent work in language modeling has led to effective SLMs with impressive performance levels across various benchmarks. |
| Approach: | They propose a benchmark that introduces process-level evaluation for commonsense reasoning tasks. |
| Outcome: | The proposed benchmarks show that large language models provide correct answers despite flawed reasoning processes in a substantial portion of cases. |