Kseniia Titova
2025
Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images
Elisei Rykov
|
Kseniia Petrushina
|
Kseniia Titova
|
Anton Razzhigaev
|
Alexander Panchenko
|
Vasily Konovalov
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
Measuring how real images look is a complex task in artificial intelligence research. For example, an image of Albert Einstein holding a smartphone violates common-sense because modern smartphone were invented after Einstein’s death. We introduce a novel method, which we called Through the Looking Glass (TLG), to assess image common sense consistency using Large Vision-Language Models (LVLMs) and Transformer-based encoder. By leveraging LVLM to extract atomic facts from these images, we obtain a mix of accurate facts. We proceed by fine-tuning a compact attention-pooling classifier over encoded atomic facts. Our TLG has achieved a new state-of-the-art performance on the WHOOPS! and WEIRD datasets while leveraging a compact fine-tuning component.
2024
SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection
Elisei Rykov
|
Yana Shishkina
|
Kseniia Petrushina
|
Kseniia Titova
|
Sergey Petrakov
|
Alexander Panchenko
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions with reference standards, encompassing diverse baselines, the refinement of pre-trained encoders through supervised learning, and an ensemble approaches utilizing several high-performing models. Through these explorations, we introduce three distinct methods that exhibit strong performance metrics. To amplify our training data, we generate additional training samples from unlabelled training subset. Furthermore, we provide a detailed comparative analysis of our approaches. Notably, our premier method achieved a commendable 9th place in the competition’s model-agnostic track and 20th place in model-aware track, highlighting its effectiveness and potential.
Search
Fix data
Co-authors
- Alexander Panchenko 2
- Kseniia Petrushina 2
- Elisei Rykov 2
- Vasily Konovalov 1
- Sergey Petrakov 1
- show all...