4 research outputs found
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
There is a growing interest in applying pre-trained large language models
(LLMs) to planning problems. However, methods that use LLMs directly as
planners are currently impractical due to several factors, including limited
correctness of plans, strong reliance on feedback from interactions with
simulators or even the actual environment, and the inefficiency in utilizing
human feedback. In this work, we introduce a novel alternative paradigm that
constructs an explicit world (domain) model in planning domain definition
language (PDDL) and then uses it to plan with sound domain-independent
planners. To address the fact that LLMs may not generate a fully functional
PDDL model initially, we employ LLMs as an interface between PDDL and sources
of corrective feedback, such as PDDL validators and humans. For users who lack
a background in PDDL, we show that LLMs can translate PDDL into natural
language and effectively encode corrective feedback back to the underlying
domain model. Our framework not only enjoys the correctness guarantee offered
by the external planners but also reduces human involvement by allowing users
to correct domain models at the beginning, rather than inspecting and
correcting (through interactive prompting) every generated plan as in previous
work. On two IPC domains and a Household domain that is more complicated than
commonly used benchmarks such as ALFWorld, we demonstrate that GPT-4 can be
leveraged to produce high-quality PDDL models for over 40 actions, and the
corrected PDDL models are then used to successfully solve 48 challenging
planning tasks. Resources including the source code will be released at:
https://guansuns.github.io/pages/llm-dm
Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change)
Recent advances in large language models (LLMs) have transformed the field of
natural language processing (NLP). From GPT-3 to PaLM, the state-of-the-art
performance on natural language tasks is being pushed forward with every new
large language model. Along with natural language abilities, there has been a
significant interest in understanding whether such models exhibit reasoning
capabilities with the use of reasoning benchmarks. However, even though results
are seemingly positive, these benchmarks prove to be simplistic in nature and
the performance of LLMs on these benchmarks cannot be used as evidence to
support, many a times outlandish, claims being made about LLMs' reasoning
capabilities. Further, these only represent a very limited set of simple
reasoning tasks and we need to look at more sophisticated reasoning problems if
we are to measure the true limits of such LLM-based systems. Motivated by this,
we propose an extensible assessment framework to test the capabilities of LLMs
on reasoning about actions and change, a central aspect of human intelligence.
We provide multiple test cases that are more involved than any of the
previously established benchmarks and each test case evaluates a different
aspect of reasoning about actions and change. Results on GPT-3 (davinci),
Instruct-GPT3 (text-davinci-002) and BLOOM (176B), showcase subpar performance
on such reasoning tasks.Comment: An updated version of this work is here: arXiv:2302.06706 Accepted at
Foundation Models for Decision Making Workshop at Neural Information
Processing Systems, 202
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on
general web corpora, in this paper, we set out to investigate their planning
capabilities. We aim to evaluate (1) how good LLMs are by themselves in
generating and validating simple plans in commonsense planning tasks (of the
type that humans are generally quite good at) and (2) how good LLMs are in
being a source of heuristic guidance for other agents--either AI planners or
human planners--in their planning tasks. To investigate these questions in a
systematic rather than anecdotal manner, we start by developing a benchmark
suite based on the kinds of domains employed in the International Planning
Competition. On this benchmark, we evaluate LLMs in three modes: autonomous,
heuristic and human-in-the-loop. Our results show that LLM's ability to
autonomously generate executable plans is quite meager, averaging only about 3%
success rate. The heuristic and human-in-the-loop modes show slightly more
promise. In addition to these results, we also make our benchmark and
evaluation tools available to support investigations by research community.Comment: arXiv admin note: text overlap with arXiv:2206.1049
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions
Decision support systems seek to enable informed decision-making. In the recent years, automated planning techniques have been leveraged to empower such systems to better aid the human-in-the-loop. The central idea for such decision support systems is to augment the capabilities of the human-in-the-loop with automated planning techniques and enhance the quality of decision-making. In addition to providing planning support, effective decision support systems must be able to provide intuitive explanations based on specific user queries for proposed decisions to its end users. Using this as motivation, we present our decision support system RADAR-X that showcases the ability to engage the user in an interactive explanatory dialogue by first enabling them to specify an alternative to a proposed decision (which we refer to as foils), and then providing contrastive explanations to these user-specified foils which helps the user understand why a specific plan was chosen over the alternative (or foil). Furthermore, the system uses this dialogue to elicit the user's latent preferences and provides revised plan suggestions through three different interaction strategies