Researchers are exploring methods to improve uncertainty estimation in large language models (LLMs) across various languages and tasks. One study found that prompting LLMs to reason in English, even when questions are in low-resource languages, significantly enhances uncertainty estimation performance. Another paper proposes a framework to decompose LLM uncertainty into input ambiguity, knowledge gaps, and decoding randomness, offering a more nuanced understanding for auditing reliability. Additionally, a new approach uses knowledge distillation to create efficient, single-pass LLMs for uncertainty estimation, achieving comparable performance to more computationally intensive methods. AI
IMPACT These studies aim to improve the reliability and trustworthiness of LLMs by enabling them to recognize and quantify their own uncertainty, which is crucial for safe deployment in critical applications.
RANK_REASON The cluster consists of multiple academic papers published on arXiv concerning LLM uncertainty estimation.
- Aditya Taparia
- arXiv
- Bayes' theorem
- Dirichlet
- Large Language Models
- Lora
- Tomasz Kuśmierczyk
- alphaXiv
- CatalyzeX
- DagsHub
- English
- Gotit.pub
- high-resource languages
- Hugging Face
- LLMs
- low-resource languages
- ScienceCast
- Uncertainty estimation
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