Adaptive Unlearning suppresses package hallucinations in code-generating LLMs by 81% while preserving benchmark performance, using model-generated data and no human labels.
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CausalSynth combines structural causal models with LLMs and iterative verification to produce synthetic data that respects given causal structures while remaining linguistically natural.
MEP uses LLMs in a structured reasoning cycle to evolve improved heuristics for HGS on VRPs, achieving up to 2.7% better solution quality and over 45% reduced runtime.
citing papers explorer
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LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning
Adaptive Unlearning suppresses package hallucinations in code-generating LLMs by 81% while preserving benchmark performance, using model-generated data and no human labels.
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CasualSynth: Generating Structurally Sound Synthetic Data
CausalSynth combines structural causal models with LLMs and iterative verification to produce synthetic data that respects given causal structures while remaining linguistically natural.
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PyVRP$^+$: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems
MEP uses LLMs in a structured reasoning cycle to evolve improved heuristics for HGS on VRPs, achieving up to 2.7% better solution quality and over 45% reduced runtime.