Research map / Large language models / Language, safety and interpretability
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Within-Model vs Between-Prompt Variability in Large Language Models for Creative Tasks (NeurIPS 2026 · similarity 0.88)
- Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond) (NeurIPS 2025 · similarity 0.88)
- More Is Not More: What Matters for Diversity in LLM Opinions？ (NeurIPS 2026 · similarity 0.87)
- Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting (ICLR 2024 · similarity 0.87)
- Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models (ICLR 2024 · similarity 0.87)
- Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing (ICLR 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.