TypeAR

What Jev does in TypeAR

The project describes itself this way: Type-safe one-decision-per-token decoding engine for autoregressive LLMs, inspired by Jev. In the Benchmarks & research category, Jev typically decides labels and probabilities on fixed test sets, so accuracy, calibration and latency can be measured.

Verification

Verified. We confirmed this repository and its Jev relevance through the GitHub API and the source lists below. A specific file link has not been recorded yet.

Topics

• ai
• jev
• llms
• types

How Benchmarks & research projects use Jev

Benchmarks and research projects test Jev rather than just use it. They compare it with language models and other classifiers, check whether its probabilities are well calibrated, and explore where the decision-model approach breaks down.

Related Jev projects in Benchmarks & research

1. typesafe-ai-benchmarkiammrduncan/typesafe-ai-benchmarkCompares Jev with other structured-output models on shared application tasks, recording errors, latency, Tokens, and estimated cost.★ 38
   TypeScript
2. jev-curateAkashPriyadarshii/jev-curateA Rust dataset-filtering experiment using Jev scores to keep or reject text records.★ 26
   Rust
3. jevifyaltryne/jevifyAn Agent Skill for finding suitable Jev decision points and designing questions and comparison experiments.★ 24
   Python
4. jev-benchmarksAbdelStark/jev-benchmarksA benchmark comparing Jev and GLiNER on text classification, probability calibration and selective automation.★ 17
   Python
5. jevcalabhixhek/jevcalA toolkit for evaluating Jev probabilities on labeled data, selecting confidence thresholds and checking model drift.★ 10
   Python
6. jev-lmy0usaf/jev-lmA word-level generation experiment that asks Jev to select words or verify locally drafted continuations.★ 6
   TypeScript

See all 107 Benchmarks & research projects →

Where we found it

• heyjunpenn/awesome-jev