Research · Updated 6 Oct, 09:30 am IST

Paper benchmarks general decision models against LLMs with JEVal

Why it matters for readers: It explains that some AI models are cheaper and fast but may be wrong more often on hard or multi-step tasks.

The authors release JEVal, a bilingual benchmark containing 11,257 instances from 36 datasets spanning 10 application domains.1
They evaluate 25 model configurations including general decision models (like Jev) and generative LLMs.1
Decision models perform well when decisions can be resolved from available evidence but weaken for tasks requiring specialist knowledge or faithful uncertainty estimates, often overstating outcome probabilities.1
In long-horizon, multi-step systems, faster local decision making reduces median episode time but lowers task success as errors accumulate over trajectories.1
In large-scale social simulation, decision models approach strong LLMs on individual response prediction at much lower inference cost but are weaker at user profiling and show larger aggregate estimation errors and systematic bias.1

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