Papers by Ilya Gusev
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 . |
HeadlineCause: A Dataset of News Headlines for Detecting Causalities (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets focus on commonsense causal reasoning or explicit causal relations . authors present dataset for detecting implicit causal relations between news headlines . |
| Approach: | They present a dataset for detecting implicit causal relations between news headlines . they use 5000 headline pairs from English news and 9000 from Russian news . |
| Outcome: | The proposed dataset shows that it is valid and can be used to predict implicit causal relations between headline pairs. |
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. |