Cristina Mahanta

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2papers

2 Papers

CLDec 17, 2024
DateLogicQA: Benchmarking Temporal Biases in Large Language Models

Gagan Bhatia, MingZe Tang, Cristina Mahanta et al.

This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing reasoning outputs. Our findings provide a comprehensive evaluation of LLMs' capabilities and limitations in temporal reasoning, highlighting key challenges in handling temporal data accurately.

CVJun 16, 2025
Leveraging Vision-Language Pre-training for Human Activity Recognition in Still Images

Cristina Mahanta, Gagan Bhatia

Recognising human activity in a single photo enables indexing, safety and assistive applications, yet lacks motion cues. Using 285 MSCOCO images labelled as walking, running, sitting, and standing, scratch CNNs scored 41% accuracy. Fine-tuning multimodal CLIP raised this to 76%, demonstrating that contrastive vision-language pre-training decisively improves still-image action recognition in real-world deployments.