Key papers

• Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles (2026)
  Donghwan Kim
  5.01
• LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring (2026)
  Unggi Lee et al.
  4.27
• Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models (2025)
  Sonam Gupta et al.
  4.25
• BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025)
  Xu Huang et al.
  2.87
• Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"? (2026)
  Germ\'an T. Eizaguirre et al.
  1.72
• Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval (2025)
  Subhendu Khatuya et al.
  1.50
• LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization (2025)
  Suchen Liu et al.
  1.33
• Can LLMs Solve ASP Problems? Insights from a Benchmarking Study (Extended Version) (2025)
  Lin Ren et al.
  1.33
• Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validation (2025)
  Yang Wu et al.
  1.28
• PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines (2025)
  Reya Vir et al.
  1.28
• Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling (2025)
  Hao Mark Chen et al.
  1.22
• R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing (2025)
  Tianyu Fu et al.
  1.22
• Distill-C: Enhanced NL2SQL via Distilled Customization with LLMs (2025)
  Cong Duy Vu Hoang et al.
  1.17
• xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference (2025)
  Maximilian Beck et al.
  1.11
• Large-Scale Data Selection for Instruction Tuning (2025)
  Hamish Ivison et al.
  1.11
• SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters (2025)
  Teng Xiao et al.
  1.06
• Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting (2023)
  Aseem Arora et al.
  —
• Selective Demonstrations for Cross-domain Text-to-SQL (2023)
  Shuaichen Chang et al.
  —
• CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning (2023)
  Yilun Liu et al.
  —
• Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs (2024)
  Yeonhong Park et al.
  —
• Is In-Context Learning Sufficient for Instruction Following in LLMs? (2024)
  Hao Zhao et al.
  —
• Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performance (2024)
  Niklas Wretblad et al.
  —
• LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale (2024)
  Jaehong Cho et al.
  —
• LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference (2024)
  Zhiwen Mo et al.
  —
• Data-Prep-Kit: getting your data ready for LLM application development (2024)
  David Wood et al.
  —
• Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement (2024)
  Shouvon Sarker et al.
  —
• Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use (2024)
  Menna Fateen et al.
  —
• Advancing LLM Reasoning Generalists with Preference Trees (2024)
  Lifan Yuan et al.
  —
• CodecLM: Aligning Language Models with Tailored Synthetic Data (2024)
  Zifeng Wang et al.
  —
• Xwin-LM: Strong and Scalable Alignment Practice for LLMs (2024)
  Bolin Ni et al.
  —
• Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs (2024)
  Xin Lai et al.
  —
• Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction (2024)
  Zhenmei Shi et al.
  —
• CAD-Recode: Reverse Engineering CAD Code from Point Clouds (2024)
  Danila Rukhovich et al.
  —