English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Generating plans of action, and reasoning about change have long beenconsidered a core competence of intelligent agents. It is thus no surprise thatevaluating the planning and reasoning capabilities of large language models(LLMs) has become a hot topic of research. Most claims about LLM planningcapabilities are however based on common sense tasks-where it becomes hard totell whether LLMs are planning or merely retrieving from their vast worldknowledge. There is a strong need for systematic and extensible planningbenchmarks with sufficient diversity to evaluate whether LLMs have innateplanning capabilities. Motivated by this, we propose PlanBench, an extensiblebenchmark suite based on the kinds of domains used in the automated planningcommunity, especially in the International Planning Competition, to test thecapabilities of LLMs in planning or reasoning about actions and change.PlanBench provides sufficient diversity in both the task domains and thespecific planning capabilities. Our studies also show that on many criticalcapabilities-including plan generation-LLM performance falls quite short, evenwith the SOTA models. PlanBench can thus function as a useful marker ofprogress of LLMs in planning and reasoning.

PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change