MetaEvaluator meta-learns an initialization from reference models to enable accurate, label-free performance estimation for unseen models across architectures and modalities.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PUDA enables effective promotion of unpopular target items in black-box LLM sequential recommenders by using evolutionary LLM refinement to infer hidden prompts, training a surrogate model, and combining adversarial text revision with surrogate-generated poisoning sequences.
citing papers explorer
-
Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
MetaEvaluator meta-learns an initialization from reference models to enable accurate, label-free performance estimation for unseen models across architectures and modalities.
-
Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems
PUDA enables effective promotion of unpopular target items in black-box LLM sequential recommenders by using evolutionary LLM refinement to infer hidden prompts, training a surrogate model, and combining adversarial text revision with surrogate-generated poisoning sequences.