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LLMs show high accuracy in translating natural language to PDDL, with Gemini 2.5 Flash leading

By PulseAugur Editorial · [1 sources] ·

A new paper evaluates the effectiveness of Large Language Models (LLMs) in translating natural language testing goals into PDDL (Planning Domain Definition Language) for automated planning. The study found that contemporary LLMs demonstrate high correctness, with Gemini 2.5 Flash achieving 96% accuracy and GPT-4.1 leading in response speed. While significant progress has been made, challenges remain due to language ambiguity and limitations in domain representation. AI

IMPACT Enhances the potential for LLMs to bridge the gap between human intent and machine execution in automated planning systems.

RANK_REASON The cluster contains an academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show high accuracy in translating natural language to PDDL, with Gemini 2.5 Flash leading

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The cluster contains an academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tomas Balyo, Lukas Chrpa, G. Michael Youngblood ·

    Which LLM is Best for Translating Natural Language Goals to PDDL

    arXiv:2609.18731v1 Announce Type: new Abstract: Bridging the gap between human intent and machine execution remains a challenge in automated planning, where expressing goals in formal languages like PDDL restricts accessibility to non-experts. This paper empirically evaluates whe…

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