Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
Automatic prompt augmentation and selection with chain-of-thought from labeled data
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
ShadowCoT introduces a reasoning-level backdoor attack on LLMs achieving 94.4% attack success rate and 88.4% hijacking success rate with 0.15% parameter updates via internal state conditioning and reasoning chain pollution.
MathFlow decouples perception and inference stages in MLLMs for visual math, with a dedicated perception model delivering gains on the FlowVerse benchmark when paired with existing reasoners.
Auto-CoT automatically generates and filters reasoning-enhanced demonstrations to improve in-context learning accuracy on complex reasoning tasks.
A two-stage static-then-dynamic prompt selection strategy using prosodic features, LLM coherence scores, and similarity metrics improves emotion intensity and speaker consistency in zero-shot TTS.
citing papers explorer
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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
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ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs
ShadowCoT introduces a reasoning-level backdoor attack on LLMs achieving 94.4% attack success rate and 88.4% hijacking success rate with 0.15% parameter updates via internal state conditioning and reasoning chain pollution.
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MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems
MathFlow decouples perception and inference stages in MLLMs for visual math, with a dedicated perception model delivering gains on the FlowVerse benchmark when paired with existing reasoners.
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ACIL: Auto Chain of Thoughts for In-Context Learning
Auto-CoT automatically generates and filters reasoning-enhanced demonstrations to improve in-context learning accuracy on complex reasoning tasks.
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Expressive Prompting: Improving Emotion Intensity and Speaker Consistency in Zero-Shot TTS
A two-stage static-then-dynamic prompt selection strategy using prosodic features, LLM coherence scores, and similarity metrics improves emotion intensity and speaker consistency in zero-shot TTS.