LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them

arXiv:2610.02076 · cs.CL · Submitted 2026-10-01 · Read on arXiv

cs.CL

Submitted: 2026-10-01

Updated: 2026-10-04

Code: https://github.com/allebee/jevk5

Terminology

• Accuracy — 61× in this paper · explained in 1 episode
• Benchmark — 36× in this paper · explained in 2 episodes
• Fine-Tuning — 35× in this paper · explained in 5 episodes
• Calibration — 21× in this paper · explained in 7 episodes
• Verification — 15× in this paper · explained in 2 episodes
• Adaptation — 13× in this paper · explained in 1 episode
• Regularization — 13× in this paper · explained in 3 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Foundation Model — 7× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Perplexity — 4× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Failure Modes — 3× in this paper · explained in 3 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Thinking mode — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• LoRA Learns Less and Forgets Less
• Lessons from the Trenches on Reproducible Evaluation of Language Models
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• Language Models (Mostly) Know What They Know
• Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions
• MuSiQue: Multihop Questions via Single-hop Question Composition
• Qwen3 Technical Report
• PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
• Instruction-Following Evaluation for Large Language Models

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering