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References (38)

[1]
Optimized Batch Prompting for Cost-effective LLMs
2025Zhaoxuan Ji, Xinlu Wang et al.
[2]
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models
2025Artem Vazhentsev, Lyudmila Rvanova et al.
[3]
Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study over Open-ended Question Answering
2024Yuan Sui, Bryan Hooi
[4]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[5]
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
2024Shicheng Liu, Sina J. Semnani et al.
[6]
LUQ: Long-text Uncertainty Quantification for LLMs
2024Caiqi Zhang, Fangyu Liu et al.
[7]
Long-form factuality in large language models
2024Jerry Wei, Chengrun Yang et al.
[8]
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
2024Roman Vashurin, Ekaterina Fadeeva et al.
[9]
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
2024Ekaterina Fadeeva, Aleksandr Rubashevskii et al.
[10]
Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
2024Shangbin Feng, Weijia Shi et al.
[11]
Benchmarking LLMs via Uncertainty Quantification
2024Fanghua Ye, Mingming Yang et al.
[12]
Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers
2024G. Yona, Roee Aharoni et al.
[13]
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
2024S. Tonmoy, S. M. M. Zaman et al.
[14]
LLMs Cannot (Yet) Match the Specificity and Simplicity of Online Communities in Long Form Question Answering
2024Kris-Fillip Kahl, Tolga Buz et al.
[15]
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
2023Lei Huang, Weijiang Yu et al.
[16]
A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation
2023Neeraj Varshney, Wenlin Yao et al.
[17]
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
2023Zhen Lin, Shubhendu Trivedi et al.
[18]
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
2023Sewon Min, Kalpesh Krishna et al.
[19]
Fact-Checking Complex Claims with Program-Guided Reasoning
2023Liangming Pan, Xiaobao Wu et al.
[20]
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers
2023Ido Galil, Mohammed Dabbah et al.

Showing 20 of 38 references

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