Commonsense reasoning often involves evaluating multiple plausible interpretations rather than selecting a single atomic answer, yet most benchmarks rely on single-label evaluation, obscuring whether statements are jointly plausible, mutually exclusive, or jointly implausible. We introduce LOGICAL-COMMONSENSEQA, a benchmark that re-frames commonsense reasoning as logical composition over pairs of atomic statements using plausibility-level operators (AND, OR, NEITHER/NOR). Evaluating instruction-tuned, reasoning-specialized, and fine-tuned models under zero-shot, few-shot, and chain-of-thought prompting, we find that while models perform reasonably on conjunctive and moderately on disjunctive reasoning, performance degrades sharply on negation-based questions. LOGICAL-COMMONSENSEQA exposes fundamental reasoning limitations and provides a controlled framework for advancing compositional commonsense reasoning.
@article{junias2026logical,title={LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning},author={Junias, Obed and Pacheco, Maria Leonor},journal={arXiv preprint arXiv:2601.16504},year={2026},}
2025
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches
Obed Junias, Prajakta Kini, and Theodora Chaspari
In 2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2025
@inproceedings{junias2025assessing,title={Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches},author={Junias, Obed and Kini, Prajakta and Chaspari, Theodora},booktitle={2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)},pages={1--7},year={2025},organization={IEEE},doi={10.1109/BHI.2025.11269509},url={https://ieeexplore.ieee.org/abstract/document/11269509},}