Papers by Moran Beladev
HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval (2025.acl-long)
Copied to clipboard
| Challenge: | a novel multimodal dense retrieval model for the travel domain addresses limitations of traditional search engines. |
| Approach: | They propose a multimodal dense retrieval model that enables natural language property search . they propose combining a small language model and a large language model for embedding hotel data . |
| Outcome: | The proposed model outperforms state-of-the-art models on four diverse test sets . it is generalizable across LLM architectures and scalability for processing large image galleries . |
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities (2023.emnlp-industry)
Copied to clipboard
Fengjun Wang, Moran Beladev, Ofri Kleinfeld, Elina Frayerman, Tal Shachar, Eran Fainman, Karen Lastmann Assaraf, Sarai Mizrachi, Benjamin Wang
| Challenge: | In the digital age, large-scale online travel platforms face the challenge of extracting valuable insights from massive volumes of textual data. |
| Approach: | They propose a model that uses a bi-encoder transformer architecture to extract structured information from textual data. |
| Outcome: | The proposed model outperforms state-of-the-art models with 92.9% micro mAP and 75.8% macro mA score compared to baseline models . the proposed model can be used to find hotel facilities and hotel rooms based on positive reviews . |
Speed Without Sacrifice: Fine-Tuning Language Models with Medusa and Knowledge Distillation in Travel Applications (2025.acl-industry)
Copied to clipboard
Daniel Zagyva, Emmanouil Stergiadis, Laurens Van Der Maas, Aleksandra Dokic, Eran Fainman, Ilya Gusev, Moran Beladev
| Challenge: | Rapid growth of digital applications has intensified the demand for real-time natural language processing (NLP) capabilities. |
| Approach: | They propose a framework that combines Medusa and knowledge distillation to achieve compounded benefits in both model size and inference speed. |
| Outcome: | The proposed framework reduces inference latency by 10-20x while maintaining the student model’s performance quality. |