15 citations · 16 across the 5 of their papers we have counts for
5 papers
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev +12
The rapid proliferation of large language models (LLMs) has stimulated researchers to seek effective and efficient approaches to deal with LLM hallucinations and low-quality output…
SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection
Elisei Rykov, Yana Shishkina, Kseniia Petrushina +3
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…
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov +9
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccura…
LM-Polygraph: Uncertainty Estimation for Language Models
Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun +9
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant cha…
Robust representations of oil wells' intervals via sparse attention mechanism
Alina Ermilova, Nikita Baramiia, Valerii Kornilov +2
Transformer-based neural network architectures achieve state-of-the-art results in different domains, from natural language processing (NLP) to computer vision (CV). The key idea o…