5 papers
Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez +1
Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models by aggregating multiple sampled outputs, but it comes at a high computati…
VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification
Sumaya Abdul Rahman, Seckhen Ariel Andrade Cuellar, Ghani Raissov +1
Natural language interfaces can greatly benefit the accessibility and usability of optimization modeling, and recent advances in large language models (LLMs) show promise in automa…
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?
Azmine Toushik Wasi, Wahid Faisal, Abdur Rahman +12
Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision-language models (VLMs). Prior work largely relied on syntheti…
Do I Really Know? Learning Factual Self-Verification for Hallucination Reduction
Enes Altinisik, Masoomali Fatehkia, Fatih Deniz +4
Factual hallucination remains a central challenge for large language models (LLMs). Existing mitigation approaches primarily rely on either external post-hoc verification or mappin…
Instantiation-based Formalization of Logical Reasoning Tasks using Language Models and Logical Solvers
Mohammad Raza, Natasa Milic-Frayling
Robustness of reasoning remains a significant challenge for large language models, and addressing it is essential for the practical applicability of AI-driven reasoning systems. We…