TL;DR: We propose controllable counterfactuals (CoCo) to evaluate dialogue state tracking (DST) models on novel scenarios, which results in significant performance drop of up to 30.8% for state-of-the-art DST models. Using CoCo for…
author name
Semih Yavuz
author title Research Director
My Background
Semih Yavuz is a Research Director at Salesforce AI Research, leading a team focused on improving the factuality, groundedness, and reasoning capabilities of large language models in knowledge-intensive applications. His work involves developing state-of-the-art embedding and re-ranker models for knowledge retrieval across diverse domains, including code, multi-modal, and multilingual contexts, while refining retrieval-augmented generation (RAG) by enhancing how LLMs consume and integrate knowledge in complex reasoning. His team is focused on pushing the boundaries of the research to develop accurate, scalable, and reliable AI systems and driving product impact with them in the CRM domain.
