PPaperPicks

Ximing Lu

21 papers at tracked venues · 14 at CORE A* · active 20242025

Venues

Frequent coauthors

Papers

  1. AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text
    ICLR 2025 · Ximing Lu
  2. Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation
  3. CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
  4. Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
  5. Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning
  6. ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
  7. Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions
  8. Synthetic Visual Genome
  9. How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
  10. Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model
  11. In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
  12. JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
  13. Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
  14. Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
  15. Position: A Roadmap to Pluralistic Alignment
  16. StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements
  17. Tailoring Self-Rationalizers with Multi-Reward Distillation
  18. The Generative AI Paradox: "What It Can Create, It May Not Understand"
  19. The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
  20. Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
  21. WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models