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Found 4 papers, 1 papers with code
SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection
In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task.
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
LM-Polygraph: Uncertainty Estimation for Language Models
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields.
Robust representations of oil wells' intervals via sparse attention mechanism
The focus in our experiments is on oil&gas data, namely, well logs, a prominent example of multivariate time series.
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