arXiv AI
One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction
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arXiv AI
Jun 9
Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level
arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.
arXiv Machine Learning
Sep 3
Prompt-Space Meta-Learning Does Not Transfer Across Users: A Frozen-LLM Negative Result
The paper investigates whether a frozen large language model can be personalized to individual users via prompt-space meta‑learning. Using the Muse framework, the authors evolve a shared adaptation prompt across a meta‑train user population and test it zero‑shot on over 200 held‑out users in two personalization benchmarks (LaMP‑2 and LaMP‑3). The results show that Muse does not outperform its un‑evolved seed prompt or a control that trains on mismatched user‑support pairs, and it is outperformed by simple few‑shot retrieval on the rating task. The authors attribute this failure to a meta‑objective collapse, where the validation objective is invariant to genuine user‑support correspondence, leading to over‑optimization of instruction polish rather than transferable adaptation.
arXiv Computation and Language
3d ago
LLM Persona Unlearning
arXiv:2609.39882v1 Announce Type: new Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-...
arXiv Machine Learning
Jun 24
Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control
arXiv:2601. 02896v3 Announce Type: replace Abstract: Controlling emergent behavioral personas (e.
arXiv AI
Aug 24
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality
The paper investigates how small lexical changes in prompts can cause large performance swings in large language models. Using a dataset of 132,000 prompt variants, the authors uncover a scaling law linking higher average task performance to lower variance and greater robustness. They identify domain-specific terminology and explicit action directives as key linguistic factors that stabilize prompts, and propose an automated Prompt-Refining Agent that reduces performance variance by 40.7% in code generation while maintaining or improving mean performance.
arXiv Machine Learning
Aug 28
$p1$: Better Prompt Optimization with Fewer Prompts
The paper investigates why prompt optimization works better for some tasks than others by decomposing reward variance into response variance and system‑prompt variance. It finds that optimization succeeds when system‑prompt variance dominates, and that adding more user prompts can actually reduce this variance, especially on heterogeneous datasets. To address this, the authors propose $p1$, a filtering method that selects a small set of high‑variance user prompts, which improves optimization on reasoning benchmarks and even allows a system prompt trained on just two AIME 24 prompts to generalize well.