Recently, there has been growing interest within the community regarding
whether large language models are capable of planning or executing plans.
However, most prior studies use LLMs to generate high-level plans for
simplified scenarios lacking linguistic complexity and domain diversity,
limiting analysis of their planning abilities. These setups constrain
evaluation methods (e.g., predefined action space), architectural choices
(e.g., only generative models), and overlook the linguistic nuances essential
for realistic analysis. To tackle this, we present PARADISE, an abductive
reasoning task using Q\&A format on practical procedural text sourced from
wikiHow. It involves warning and tip inference tasks directly associated with
goals, excluding intermediary steps, with the aim of testing the ability of the
models to infer implicit knowledge of the plan solely from the given goal. Our
experiments, utilizing fine-tuned language models and zero-shot prompting,
reveal the effectiveness of task-specific small models over large language
models in most scenarios. Despite advancements, all models fall short of human
performance. Notably, our analysis uncovers intriguing insights, such as
variations in model behavior with dropped keywords, struggles of BERT-family
and GPT-4 with physical and abstract goals, and the proposed tasks offering
valuable prior knowledge for other unseen procedural tasks. The PARADISE
dataset and associated resources are publicly available for further research
exploration with this https URL

使用 PARADISE 数据集和任务，探讨大型语言模型在实际过程文本中推理和计划能力的强弱，发现使用任务特定的小型模型在大多数情况下效果更好，但所有模型都无法与人类表现相媲美。

用程式化警告和提示数据集评估语言模型的隐性规划技能

PARADISE: Evaluating Implicit Planning Skills of Language Models with  Procedural Warnings and Tips Dataset

Recent advancements in reasoning abilities of Large Language Models (LLM) has
promoted their usage in problems that require high-level planning for robots
and artificial agents. However, current techniques that utilize LLMs for such
planning tasks make certain key assumptions such as, access to datasets that
permit finetuning, meticulously engineered prompts that only provide relevant
and essential information to the LLM, and most importantly, a deterministic
approach to allow execution of the LLM responses either in the form of existing
policies or plan operators. In this work, we propose LgTS (LLM-guided
Teacher-Student learning), a novel approach that explores the planning
abilities of LLMs to provide a graphical representation of the sub-goals to a
reinforcement learning (RL) agent that does not have access to the transition
dynamics of the environment. The RL agent uses Teacher-Student learning
algorithm to learn a set of successful policies for reaching the goal state
from the start state while simultaneously minimizing the number of
environmental interactions. Unlike previous methods that utilize LLMs, our
approach does not assume access to a propreitary or a fine-tuned LLM, nor does
it require pre-trained policies that achieve the sub-goals proposed by the LLM.
Through experiments on a gridworld based DoorKey domain and a search-and-rescue
inspired domain, we show that generating a graphical structure of sub-goals
helps in learning policies for the LLM proposed sub-goals and the
Teacher-Student learning algorithm minimizes the number of environment
interactions when the transition dynamics are unknown.

本研究提出了一种新方法，利用大型语言模型（LLM）的规划能力，为没有环境转换动力学访问权限的强化学习代理提供子目标的图形表示，同时最小化环境交互次数。

LgTS: 使用 LLM 生成的子目标进行动态任务抽样的强化学习代理

LgTS: Dynamic Task Sampling using LLM-generated sub-goals for  Reinforcement Learning Agents

Large Language Models (LLMs) have demonstrated impressive planning abilities
due to their vast "world knowledge". Yet, obtaining plans that are both
feasible (grounded in affordances) and cost-effective (in plan length), remains
a challenge, despite recent progress. This contrasts with heuristic planning
methods that employ domain knowledge (formalized in action models such as PDDL)
and heuristic search to generate feasible, optimal plans. Inspired by this, we
propose to combine the power of LLMs and heuristic planning by leveraging the
world knowledge of LLMs and the principles of heuristic search. Our approach,
SayCanPay, employs LLMs to generate actions (Say) guided by learnable domain
knowledge, that evaluates actions' feasibility (Can) and long-term
reward/payoff (Pay), and heuristic search to select the best sequence of
actions. Our contributions are (1) a novel framing of the LLM planning problem
in the context of heuristic planning, (2) integrating grounding and
cost-effective elements into the generated plans, and (3) using heuristic
search over actions. Our extensive evaluations show that our model surpasses
other LLM planning approaches.

结合大型语言模型和启发式规划的方法，该研究提出了一种通过利用大型语言模型的世界知识和启发式搜索原则来生成行动序列的新方法，该方法能够在规划过程中考虑行动的可行性和成本效益，评估行动的长期效果，并在广泛的评估中显示优于其他大型语言模型规划方法。