Yana Shishkina


2024

pdf bib
SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection
Elisei Rykov | Yana Shishkina | Ksenia Petrushina | Ksenia 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.

2023

pdf bib
DeepPavlov Dream: Platform for Building Generative AI Assistants
Diliara Zharikova | Daniel Kornev | Fedor Ignatov | Maxim Talimanchuk | Dmitry Evseev | Ksenya Petukhova | Veronika Smilga | Dmitry Karpov | Yana Shishkina | Dmitry Kosenko | Mikhail Burtsev
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

An open-source DeepPavlov Dream Platform is specifically tailored for development of complex dialog systems like Generative AI Assistants. The stack prioritizes efficiency, modularity, scalability, and extensibility with the goal to make it easier to develop complex dialog systems from scratch. It supports modular approach to implementation of conversational agents enabling their development through the choice of NLP components and conversational skills from a rich library organized into the distributions of ready-for-use multi-skill AI assistant systems. In DeepPavlov Dream, multi-skill Generative AI Assistant consists of NLP components that extract features from user utterances, conversational skills that generate or retrieve a response, skill and response selectors that facilitate choice of relevant skills and the best response, as well as a conversational orchestrator that enables creation of multi-skill Generative AI Assistants scalable up to industrial grade AI assistants. The platform allows to integrate large language models into dialog pipeline, customize with prompt engineering, handle multiple prompts during the same dialog session and create simple multimodal assistants.