SynLearner lets LLMs improve synthetic data generation on later tasks in a stream by learning reusable patterns and balancing quality with diversity from feedback on earlier tasks.
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Extending textual gradients to multi-objective LLM judge optimization shows a 59% drop in gradient task-focus and a 0.085 drop in Spearman rho, due to gradient dilution at optimization time and instruction interference at inference time.
A supervised fine-tuning approach using inverted multi-resolution planning scaffolds from public-domain novels trains models to generate book-length stories with more human-like literary qualities than standard instruction-tuned LLMs.
citing papers explorer
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Make LLM Learn to Synthesize from Streaming Experiences through Feedback
SynLearner lets LLMs improve synthetic data generation on later tasks in a stream by learning reusable patterns and balancing quality with diversity from feedback on earlier tasks.
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When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges
Extending textual gradients to multi-objective LLM judge optimization shows a 59% drop in gradient task-focus and a 0.085 drop in Spearman rho, due to gradient dilution at optimization time and instruction interference at inference time.
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Towards Human-Level Book-Writing Capability
A supervised fine-tuning approach using inverted multi-resolution planning scaffolds from public-domain novels trains models to generate book-length stories with more human-like literary qualities than standard instruction-tuned LLMs.