PPaperPicks

Kan Li

Beijing Institute of Technology, School of Computer Science and Technology, China

23 papers at tracked venues · 18 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient
  2. Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labels
  3. Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation
  4. CogLM: Tracking Cognitive Development of Large Language Models
  5. Combating Semantic Contamination in Learning with Label Noise
  6. Every Rollout Counts: Optimal Resource Allocation for Efficient Test-Time Scaling
  7. From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MarkerGen
  8. InsBank: Evolving Instruction Subset for Ongoing Alignment
  9. Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning
  10. Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules
  11. Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation
  12. Silencer: From Discovery to Mitigation of Self-Bias in LLM-as-Benchmark-Generator
  13. Speculative Decoding for Multi-Sample Inference
  14. Stitch and Tell: A Structured Data Augmentation Method for Spatial Understanding
  15. UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization
  16. BatchEval: Towards Human-like Text Evaluation
  17. Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation
  18. Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
  19. Focused Large Language Models are Stable Many-Shot Learners
  20. Instruction Embedding: Latent Representations of Instructions Towards Task Identification
  21. Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation
  22. Poor-Supervised Evaluation for SuperLLM via Mutual Consistency
  23. Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data