arXiv Artificial Intelligence

NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees

2 hours ago 25.09.2026 07:00 👁 2 views
NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees

Quick summary

arXiv:2609.28587v1 Announce Type: cross Abstract: Large language models can interpret natural lan- guage, yet robust decisions remain challenging. Jev-like models expose structured choices, but these interfaces do not directly provide numeri- cal values at a requested precision. We propose NUMERICJEV, a training-free numerical decod- ing algorithm that enables numerical output from any LLM with a Jev-like structured-choice in- terface. Surprisingly, on our arithmetic bench- mark, it outperforms direct selection from a can- didate list containing the correct answer by 2.93 percentage points (Fi

Key takeaways

  • arXiv:2609.28587v1 Announce Type: cross Abstract: Large language models can interpret natural lan- guage, yet robust decisions remain challenging.
  • Jev-like models expose structured choices, but these interfaces do not directly provide numeri- cal values at a requested precision.
  • We propose NUMERICJEV, a training-free numerical decod- ing algorithm that enables numerical output from any LLM with a Jev-like structured-choice in- terface.

Why it matters

The importance of “NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗