LLMs are gaining traction as workforces across domains explore artificial intelligence and automation to plan their operations and make crucial decisions. Therefore, generative and fundamental models are relied upon for multi-step reasoning tasks to achieve planning and execution on par with humans. Although this aspiration has not yet been achieved, we need extensive and unique benchmarks to test the intelligence of our models in reasoning and decision making. Given the recent generation of ai and the short period of evolution of LLMs, it is challenging to generate validation approaches that match the pace of LLM innovations. In particular, subjective statements such as planning statements. The integrity of the validation metric may remain questionable. On the one hand, even if a model meets the checkboxes for an objective, can we determine its ability to plan? Second, in practical scenarios, there is not only a single plan but multiple plans and their alternatives. This makes the situation more chaotic. Fortunately, researchers around the world are working to improve the skills of LLMs for industrial planning. Therefore, we need a good benchmark that proves whether LLMs have achieved sufficient reasoning and planning capabilities or whether it is a distant dream.
ACBPench is an LLM reasoning assessment developed by IBM Research that consists of 7 reasoning tasks in 13 planning domains. This benchmark includes reasoning tasks necessary for reliable planning, compiled in a formal language that can reproduce more problems and scale without human interference. The name ACPBench is derived from the central topic on which its reasoning tasks focus: TOaction, dohang and Planing. The complexity of the tasks varies: some require single-step reasoning and others require multi-step reasoning. They follow Boolean and multiple choice questions (MCQs) from the 13 domains (12 are well-established benchmarks in planning and reinforcement learning, and the last one is designed from scratch). Previous benchmarks in LLM planning were limited to a few domains, causing scaling problems.
In addition to being applied across multiple domains, ACPBench differs from its contemporaries in that it generates data sets from formal descriptions of the Planning Domain Definition Language (PDDL), which is itself responsible for creating correct problems and escalating them without human intervention. .
The seven tasks presented in ACPBench are:
- Applicability: determines the valid actions among those available in a given situation.
- Progression: understanding the result of an action or change.
- Accessibility: Checks whether the model can achieve the final goal from the current state by performing multiple actions.
- Action Accessibility: Identify the prerequisites for the execution of a specific function.
- Validation: Evaluate whether the specified sequence of actions is valid, applicable, and successfully achieves the intended objective.
- Justification – Identify if an action is necessary.
- Benchmarks: Identify subgoals that are necessary to achieve the goal.
Twelve of the thirteen domains encompassed by the above tasks are dominant classical planning names, such as BlocksWorld, Logistics, and Rovers, and the last is a new category that the authors call Swap. Each of these domains has a formal representation in PDDL.
ACPBench was tested on 22 frontier and open source LLMs. Some of the famous ones included GPT-4o, CALLSmodels, Mixtraland others. The results showed that even the best performing models (GPT-4o and FLAME-3.1 405B) had problems with specific tasks, particularly in Action Accessibility and validation. Some smaller models, such as Codestral 22Bperformed well on Boolean questions but fell behind on multiple choice questions. GPT 4o's average accuracy reached 52 percent on these tasks. The post-evaluation authors also refined Granite-code 8B, a small model, and the process led to significant improvements. This fine-tuned model performed on par with massive LLMs and also generalized well to unseen domains!
ACPBench findings demonstrated that LLMs underperformed on planning tasks, regardless of their size and complexity. However, with skillfully crafted prompts and fine-tuning techniques, they can perform better in planning.
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Adeeba Alam Ansari is currently pursuing her dual degree from the Indian Institute of technology (IIT) Kharagpur, where she earned a bachelor's degree in Industrial Engineering and a master's degree in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and curious person. Adeeba firmly believes in the power of technology to empower society and promote well-being through innovative solutions driven by empathy and a deep understanding of real-world challenges.
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