The paper introduces the LLM-Modulo Framework, highlighting LLMs’ inability to generate or verify executable plans while advocating their helpful role in planning when combined with model-based verifiers.
It dissects misconceptions around LLMs’ planning and self-verification abilities, emphasizing the distinction between extracting general planning knowledge and generating executable plans.
The LLM-Modulo Framework incorporates hard and soft critics to evaluate LLM-generated plan candidates, utilizing LLMs for candidate plan generation, plan reformulation, specification refinement, and model acquisition.
Proposes that the integration of LLMs in planning tasks presents a pragmatic approach to overcoming limitations faced by traditional planning systems, advocating for a collaborative, neuro-symbolic architecture.
Recent advancements in LLMs have spurred debates regarding their capacities and roles in planning and reasoning tasks. Amidst claims oscillating between the over-optimistic and the over-pessimistic, this analysis proposes a middle ground, introducing the LLM-Modulo Framework. This framework acknowledges LLMs' inability to autonomously generate executable plans or perform plan verification but emphasizes their potential as valuable contributors when combined with external model-based verifiers. It essentially positions LLMs as powerful cognitive orthotics and universal approximate knowledge sources that, when correctly harnessed, can significantly aid in the planning process.
Studies reveal that LLMs struggle to autonomously generate executable plans, with a trivial percentage of LLM-generated plans actually reaching their goals upon autonomous mode operation.
LLMs also demonstrate a notable deficiency in plan verification, challenging the notion that they can iteratively refine solutions through self-critique.
Claims of Planning Capabilities: Misinterpretations occur when general planning knowledge extraction is mistaken for executable plan generation. Moreover, LLMs' impressive performance in certain domains is often due to the absence of complex subgoal interactions or relies on human intervention.
Self-Verification Abilities: The optimism around LLMs self-verifying their output lacks empirical support; the efficacy of iterative prompting and LLM-based critiquing in refining solutions remains unsubstantiated.
Candidate Plan Generation: Generating plausible plan candidates based on problem specifications and iterative critiques.
Reformulation: Utilizing their syntax conversion strengths to adapt plan candidates into forms digestible by various critics.
Specification Refinement and Model Acquisition: Assisting in refining problem specifications and acquiring domain models through interaction with human experts.
The LLM-Modulo Framework proposes a pragmatic approach towards leveraging LLMs in planning tasks, shifting the narrative from their solitary functionality to a collaborative, integrative use with symbolic components. It opens avenues for:
Expanding the scope of model-based planning: Integrating LLMs in planning routines can address the expressiveness and search-complexity limitations traditional planners face, offering a path towards more flexible and broadly applicable planning solutions.
Enhanced domain model acquisition: Facilitating easier extraction and refinement of planning models from unstructured knowledge sources.
The exploration into the LLM-Modulo Framework underscores a paradigm shift in utilizing LLMs for planning tasks. Instead of viewing these models as stand-alone planners or verifiers, the framework advocates for their integration within a neuro-symbolic architecture that pairs their generative capabilities with the precision of external verifiers. This balanced approach not only enhances the functionality and applicability of LLMs in complex planning scenarios but also sets a precedent for future research in the field of artificial intelligence and automated planning systems.