Papers with overall metrics
Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model (2022.emnlp-main)
Copied to clipboard
Hojun Cho, Dohee Kim, Seungwoo Ryu, ChaeHun Park, Hyungjong Noh, Jeong-in Hwang, Minseok Choi, Edward Choi, Jaegul Choo
| Challenge: | Existing methods to train text style transfer models with adversarial loss degrade fluency compared to other metrics. |
| Approach: | They propose a method which leverages a pretrained language model to improve fluency by restructuring the discriminator and the model itself. |
| Outcome: | The proposed model achieves state-of-the-art on three public benchmarks and achieved state-outperformance on the overall metrics. |
Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, but still suffer from hallucinations and clinically significant errors. |
| Approach: | They propose a grounding fixation strategy that integrates radiologist eye fixations and bounding box annotations into the LLM prompting framework. |
| Outcome: | The proposed model improves performance without retraining across domain-specific and general-purpose models and achieves an 87.3% clinical average performance. |