15 citations · 19 across the 17 of their papers we have counts for
4 papers · 1 filter
Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models
Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing +7
Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper u…
Reference-free Hallucination Detection for Large Vision-Language Models
Qing Li, Jiahui Geng, Chenyang Lyu +3
Large vision-language models (LVLMs) have made significant progress in recent years. While LVLMs exhibit excellent ability in language understanding, question answering, and conver…
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…
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…