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Collections including paper arxiv:2305.14975
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Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation
Paper • 2311.08877 • Published • 7 -
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
Paper • 2305.14975 • Published • 2 -
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
Paper • 2305.13712 • Published • 2
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R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
Paper • 2311.09677 • Published • 3 -
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
Paper • 2307.10236 • Published • 1 -
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models
Paper • 2307.01379 • Published • 1 -
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Paper • 2305.19187 • Published • 1
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Re3: Generating Longer Stories With Recursive Reprompting and Revision
Paper • 2210.06774 • Published • 2 -
Constitutional AI: Harmlessness from AI Feedback
Paper • 2212.08073 • Published • 2 -
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Paper • 2402.04253 • Published -
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Paper • 2305.19118 • Published
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Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Paper • 2212.09146 • Published • 3 -
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
Paper • 2308.10633 • Published • 1 -
MemeCap: A Dataset for Captioning and Interpreting Memes
Paper • 2305.13703 • Published -
Contrastive Learning for Inference in Dialogue
Paper • 2310.12467 • Published
-
Re3: Generating Longer Stories With Recursive Reprompting and Revision
Paper • 2210.06774 • Published • 2 -
Constitutional AI: Harmlessness from AI Feedback
Paper • 2212.08073 • Published • 2 -
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Paper • 2402.04253 • Published -
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Paper • 2305.19118 • Published
-
Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation
Paper • 2311.08877 • Published • 7 -
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
Paper • 2305.14975 • Published • 2 -
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
Paper • 2305.13712 • Published • 2
-
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Paper • 2212.09146 • Published • 3 -
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
Paper • 2308.10633 • Published • 1 -
MemeCap: A Dataset for Captioning and Interpreting Memes
Paper • 2305.13703 • Published -
Contrastive Learning for Inference in Dialogue
Paper • 2310.12467 • Published
-
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
Paper • 2311.09677 • Published • 3 -
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
Paper • 2307.10236 • Published • 1 -
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models
Paper • 2307.01379 • Published • 1 -
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Paper • 2305.19187 • Published • 1