Summary LLMP Empowering Large Language Models with Optimal Planning arxiv.org
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The LLMP framework combines large language models with classical planners to generate optimal plans for planning problems, addressing the lack of true understanding in LLMs and allowing for zero-shot generalization ability.
Key Points
- Large Language Models (LLMs) like GPT-4 lack true understanding despite their impressive zero-shot generalization abilities
- LLM+P methodology incorporates classical planners into LLMs to provide optimal solutions for planning problems
- LLM+P pipeline generates correct solutions to more planning problems than LLMs on their own
- LLMP combines LLMs with Classical Planners to empower LLMs with optimal planning capabilities
- LLMP improves LLMs' performance in solving complex planning tasks and uses Planning Domain Definition Language (PDDL) to formalize planning problems
- LLMP proposes ways to extend the framework, including enabling LLMs to auto-detect when and how to apply finetuning and reducing their dependency on human input.
Summaries
302 word summary
LLMP is a framework that combines Large Language Models (LLMs) with Classical Planners to empower LLMs with optimal planning capabilities. It uses Planning Domain Definition Language (PDDL) to formalize planning problems and leverages symbolic planners to output correct plans. LLMP uses in-context learning, which allows LLMs to perform unseen downstream tasks without fine-tuning their parameters. The system assumes that a human expert can provide a domain description in PDDL format, which is used to generate problem instances. LLMs are pretrained with a large amount of offline text, allowing for zero-shot generalization ability. Classical planning algorithms are used for complicated planning tasks, and the proposed LLM+P framework focuses on translating the planning problem from natural language to structured PDDL format and produces an optimal plan for the majority of problems. The paper discusses various planning systems and historical developments in artificial intelligence and robotics and proposes ways to extend the LLMP framework, including enabling LLMs to auto-detect when and how to apply finetuning and reducing their dependency on human input. Large language models lack true understanding despite their impressive zero-shot generalization abilities. The LLM+P methodology incorporates classical planners into LLMs to address this issue. LLM+P takes a natural language description of a planning problem and returns a correct plan for solving it in natural language. Through experiments, researchers found that LLM+P provides optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems. LLM+P connects LLMs to general-purpose planners to solve planning problems without altering their training corpus. This methodology can be applied to any class of problems with a sound and complete solver. The LLM+P pipeline generates correct solutions to more planning problems than LLMs on their own. However, the LLM is not asked to recognize prompts suitable for processing using the LLM+P pipeline in this paper.
604 word summary
Large language models (LLMs) like GPT-4 and ChatGPT lack true understanding despite their impressive zero-shot generalization abilities. The LLM+P methodology incorporates classical planners into LLMs to address this issue. LLM+P takes a natural language description of a planning problem and returns a correct plan for solving it in natural language. Through experiments, researchers found that LLM+P provides optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems. LLMs are proficient in linguistic competence but lack functional competence. LLM+P connects LLMs to general-purpose planners to solve planning problems without altering their training corpus. The LLM+P pipeline outputs a problem description, solves the problem using a general-purpose planner, and converts the output back to natural language. This methodology can be applied to any class of problems with a sound and complete solver. The LLM+P pipeline generates correct solutions to more planning problems than LLMs on their own. However, the LLM is not asked to recognize prompts suitable for processing using the LLM+P pipeline in this paper. LLMP is a system that combines Large Language Models (LLMs) with Classical Planners to empower LLMs with optimal planning capabilities. It uses Planning Domain Definition Language (PDDL) to formalize planning problems and leverages symbolic planners to output correct plans. LLMP uses in-context learning, which allows LLMs to perform unseen downstream tasks without fine-tuning their parameters. The system assumes that a human expert can provide a domain description in PDDL format, which is used to generate problem instances. LLMP provides a natural language interface for giving tasks to robot systems and automates the process of generating PDDL descriptions for problem domains. LLMs are pretrained with a large amount of offline text, allowing for zero-shot generalization ability. Classical planning algorithms are used for complicated planning tasks, and recent classical planning systems designed for robotics often use PDDL or answer set programming (ASP) as the underlying action language for the planners. The LLM+P pipeline requires a chatbot to trigger the LLM+P based on its conversation with a human user. Finally, the LLM translates the PDDL plan back into natural language. The study proposes a framework called LLMP to empower Large Language Models (LLMs) with optimal planning capabilities by combining classical planning with LLMs. Seven planning domains were utilized in the study to evaluate the proposed approach and answer three research questions. The study shows that augmenting LLMs with classical planners improves their performance in solving complex tasks. LLM+P performs significantly better than LLM-AS-P, and context plays a crucial role in the success of LLM+P. The proposed LLM+P framework focuses on translating the planning problem from natural language to structured PDDL format and produces an optimal plan for the majority of problems. The hardest domains are the ones with complex spatial relationships. The paper discusses various planning systems and historical developments in artificial intelligence and robotics and proposes ways to extend the LLMP framework, including enabling LLMs to auto-detect when and how to apply finetuning and reducing their dependency on human input. LLMP Empowering Large Language Models with Optimal Planning is an academic paper that discusses the use of language models as zero-shot planners for robot task planning and situation handling in open worlds. The paper references multiple other academic papers that explore the use of large language models for object rearrangement, task and motion planning, and multi-robot planning with conflicts. The document discusses LLMP, a method for empowering large language models with optimal planning, which involves defining actions and their effects using PDDL, a language for specifying planning problems. The paper presents three planning problems: Grippers, BlocksWorld, and Barman, and several problem scenarios and their corresponding goals.
1704 word summary
LLMP Empowering Large Language Models with Optimal Planning presents several problem scenarios and their corresponding goals. In the first problem, the goal is to replace flat tyres with intact tyres on the hubs while ensuring that the nuts are tight and the boot is closed. The second problem involves building blocks to achieve a height of 3 at a specific position on a grid. The third problem requires moving crates from a load area to a depot using hoists while ensuring that all crates and container storeareas are connected. In the fourth problem, two robots must transport two balls to their respective rooms using their grippers. LLMP Empowering Large Language Models with Optimal Planning presents three planning problems: Grippers, BlocksWorld, and Barman. The Grippers problem involves moving two balls between three rooms using three robots. The BlocksWorld problem requires making three cocktails using a shaker, three dispensers, four shot glasses, and three ingredients. The Barman problem involves providing a PDDL file that describes a planning problem and providing an optimal plan to solve the problem. The paper includes examples of representative failures for both baseline methods and LLMP-AS-P, but no further explanations are provided. The document discusses LLMP, a method for empowering large language models with optimal planning. The method involves defining actions and their effects using PDDL, a language for specifying planning problems. The resulting plans can be used to solve complex tasks, such as arranging blocks in a specific configuration. The document includes examples of PDDL actions and a sample problem domain. The document does not include any boilerplate or unrelated information. LLMP Empowering Large Language Models with Optimal Planning is an academic paper that discusses the use of language models as zero-shot planners for robot task planning and situation handling in open worlds. The paper references multiple other academic papers that explore the use of large language models for object rearrangement, task and motion planning, and multi-robot planning with conflicts. The paper also references the use of integrated task and motion planning in belief space and nonmonotonic reasoning for urban planning. Additionally, the paper mentions various language models, including OPT, BERT, and ChatGPT, and their applications in code evaluation, transformers for language understanding, and scaling language modeling with pathways. The paper discusses the LLMP framework for empowering large language models (LLMs) with optimal planning. It references various planning systems and historical developments in artificial intelligence and robotics. The paper highlights the challenges of LLMs in planning and reasoning about change and proposes ways to extend the LLMP framework, including enabling LLMs to auto-detect when and how to apply finetuning and reducing their dependency on human input. The proposed LLM+P framework aims to empower large language models with optimal planning capabilities by making them aware of a simple (problem, PDDL) pair. The key design choice is to focus LLMs on translating the planning problem from natural language to structured PDDL format. The proposed LLM+P produces an optimal plan for the majority of problems, and it is important to make LLMs aware of the context for in-context learning. The failures of LLM+P cases are due to mis-specified problem files, such as missing one of the initial conditions causing the planning problem to be unsolvable. The hardest domains are the ones with complex spatial relationship. LLM+P performs significantly better than LLM-AS-P and can solve most complicated planning problems. The success rate of applying LLM-AS-P, LLM+P without context, and LLM+P is shown in Table 1. Automatic validation software is used to validate whether the predicted problem PDDL file is correct for the given problem. LLMP provides a natural language planning system called LLM-AS-P that generates plans for various domains. However, adding context does not significantly increase the success rate, and most plans generated are not feasible. The study applied LLM-AS-P and LLM+P to seven domains, and the results showed that the found plans are near-optimal but not optimal. The experiment setup includes an automatic and procedural generation script for each domain, and each problem comes with a natural language description and a ground-truth problem PDDL file. The domains include Blocksworld, Barman, Floortile, Grippers, Storage, Terme, and Tyreworld. This document presents a study on how to empower Large Language Models (LLMs) with optimal planning capabilities. To address this issue, the authors propose combining classical planning with LLMs. They present seven planning domains that are frequently utilized in classical planning competitions and conduct extensive experiments to evaluate the proposed approach. The study aims to answer three research questions: (1) how well does LLM-AS-P work? (2) how well does LLM+P work compared to LLM-AS-P? and (3) what role does context play in the success of LLM+P? The authors propose augmenting LLMs with external modules, such as classical planners, to improve their performance in solving complex tasks. The study shows that LLM+P performs much better than LLM-AS-P and that context plays a crucial role in the success of LLM+P. Additionally, the study highlights the importance of incorporating long-horizon reasoning and knowledge from classical planners into existing LLMs. Large language models (LLMs) have been developed for task planning in household domains using natural language prompts. LLMs are pretrained with a large amount of offline text, allowing for zero-shot generalization ability. Classical planning algorithms are used for complicated planning tasks, and recent classical planning systems designed for robotics often use planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language for the planners. A new approach, LLM+P, combines classical planning in discrete spaces and robot motion planning in continuous space. The LLM+P pipeline requires a chatbot to trigger the LLM+P based on its conversation with a human user. The LLM uses in-context learning to infer the problem PDDL file corresponding to the task. Once the problem PDDL file is generated, it is fed into any classical planner, together with the provided domain PDDL file, to generate a PDDL plan. Finally, the LLM translates the PDDL plan back into natural language. The document discusses LLMP, a system that combines Large Language Models (LLMs) with Classical Planners to empower LLMs with optimal planning capabilities. The system assumes that a human expert can provide a domain description in PDDL format, which is used to generate problem instances. LLMP uses in-context learning, which allows LLMs to perform unseen downstream tasks without fine-tuning their parameters. An example problem is given involving moving blocks, and a PDDL file is generated by GPT-4 based on natural language input and context. The generated file appears to have the correct syntax but uses a made-up predicate. Overall, LLMP provides a natural language interface for giving tasks to robot systems and automates the process of generating PDDL descriptions for problem domains. The paper discusses using the Planning Domain Definition Language (PDDL) to rewrite planning prompts for Large Language Models (LLMs) to improve their long-horizon reasoning abilities. The paper provides a detailed explanation of PDDL and how it is used to formalize planning problems. It introduces the LLM+P method, which leverages symbolic planners to output correct plans. The paper assumes a factored state space and a fixed set of variables, and defines the input of a planning problem as a tuple. The paper includes a running example and provides two PDDL files: a domain file and a problem file. The paper also discusses LLM+P (In-context Learning), which uses an LLM to produce the PDDL description of a problem, then leverages a classical planner to find an optimal plan, and finally translates the raw plan back to natural language using the LLM again. The paper recaps the standard representation of classical planners and their use with LLMs. LLM+P is a methodology that connects Large Language Models (LLMs) to general-purpose planners to solve planning problems. The objective of this research is to enable LLMs to solve planning problems correctly without altering their training corpus. The LLM+P pipeline consists of three components: 1) outputs a problem description suitable as input to a general-purpose planner, 2) solves the problem using the general-purpose planner, and 3) converts the output of the planner back to natural language. While this paper focuses on planning problems, this general methodology can be applied to any class of problems for which we have a sound and complete solver. The LLM+P pipeline is able to generate correct solutions to many more planning problems than are LLMs on their own. However, in this paper, the LLM is not asked to recognize that it has been posed a prompt that is suitable for processing using the proposed LLM+P pipeline.
LLMs have become amazingly proficient at linguistic competence - knowing how to say things but not nearly as good at functional competence - knowing what to say. They are not specifically built to demonstrate understanding, but rather trained to generate sequences of words that might be plausible to a human given a prior context. Given how LLMs are designed and trained, this phenomenon should come as no surprise. LLMs can be (relatively) easily fooled by asking for the result of a straightforward arithmetic problem that does not appear in their training corpus or by asking for the solution to a novel planning problem that requires knowledge about how the world works.
The text also includes an example of a failure of GPT-4 in planning and a contribution section with a link to code and results available on GitHub. Large language models (LLMs) such as GPT-4 and ChatGPT have shown impressive zero-shot generalization abilities but lack true understanding. AI researchers have long sought to create programs that can converse in natural language with the same grace and flexibility as people. However, while even relatively simple models can generate responses to some prompts that seem reasonable, it has always been relatively easy to generate prompts that expose their weaknesses compared to people. In an effort to address this issue, the paper introduces LLM+P, which incorporates the strengths of classical planners into LLMs. LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language. Via a comprehensive set of experiments taken from common planning scenarios, the researchers found that LLM+P is able to provide optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems.