| ACL |
Do Large Language Models Know What They Don’t Know? |
2023 |
ACL |
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Do Language Models Know When They’re Hallucinating References? |
2023 |
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Language models (mostly) know what they know |
2022 |
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Knowing what llms do not know: A simple yet effective self-detection method |
2023 |
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Can AI assistants know what they don’t know? |
2024 |
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Self-knowledge guided retrieval augmentation for large language models |
2023 |
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Llm self defense: By self examination, llms know they are being tricked |
2023 |
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Confidence Regulation Neurons in Language Models |
2024 |
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Decomposing uncertainty for large language models through input clarification ensembling. |
2023 |
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Probing for uncertainty in language model latent beliefs. |
2023 |
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How can we know when language models know? on the calibration of language models for question answering. |
2021 |
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. |
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Teaching models to express their uncertainty in words. |
2022 |
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Uncertainty estimation in autoregressive structured prediction. |
2022 |
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Emergent linear structure in large language model representations of true/false datasets, |
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Locating and editing factual associations in GPT |
2022 |
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| ICLR |
Prompting GPT-3 to be reliable. |
2023 |
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Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. |
2023 |
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| Nature |
Detecting hallucinations in large language models using semantic entropy |
2024 |
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| ACL |
Calibrating the Confidence of Large Language Models by Eliciting Fidelity |
2024 |
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| ACL |
Fact-Level Confidence Calibration: Empowering Confidence-Guided LLM Self-Correction |
2024 |
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