2 papers
cs.CL2026
Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps
Jonas Waldendorf, Bashar Awwad Shiekh Hasan, Evgenii Tsymbalov
Hallucinations in Speech Large Language Models (SpeechLLMs) pose significant risks, yet existing detection methods typically rely on gold-standard outputs that are costly or imprac…
cs.CL2024
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…