Skill Learning Using Process Mining for Large Language Model Plan Generation

• URL: http://arxiv.org/abs/2410.12870v1
• Date: Mon, 14 Oct 2024 12:48:42 GMT
• Title: Skill Learning Using Process Mining for Large Language Model Plan Generation
• Authors: Andrei Cosmin Redis, Mohammadreza Fani Sani, Bahram Zarrin, Andrea Burattin,
• Abstract summary: Large language models (LLMs) hold promise for generating plans for complex tasks. Their effectiveness is limited by sequential execution, lack of control flow models, and difficulties in skill retrieval. We introduce a novel approach to skill learning in LLMs by integrating process mining techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) hold promise for generating plans for complex tasks, but their effectiveness is limited by sequential execution, lack of control flow models, and difficulties in skill retrieval. Addressing these issues is crucial for improving the efficiency and interpretability of plan generation as LLMs become more central to automation and decision-making. We introduce a novel approach to skill learning in LLMs by integrating process mining techniques, leveraging process discovery for skill acquisition, process models for skill storage, and conformance checking for skill retrieval. Our methods enhance text-based plan generation by enabling flexible skill discovery, parallel execution, and improved interpretability. Experimental results suggest the effectiveness of our approach, with our skill retrieval method surpassing state-of-the-art accuracy baselines under specific conditions.

Related papers

• Efficient Strategy for Improving Large Language Model (LLM) Capabilities [0.0]
  Large Language Models (LLMs) have become a milestone in the field of artificial intelligence and natural language processing.<n>Their large-scale deployment remains constrained by the need for significant computational resources.<n>This work proposes starting from a base model to explore and combine data processing and careful data selection techniques.
  arXiv  Detail & Related papers  (2025-08-06T04:08:26Z)
• Optimising Language Models for Downstream Tasks: A Post-Training Perspective [0.0]
  Language models (LMs) have demonstrated remarkable capabilities in NLP.<n>But adapting them efficiently and robustly to specific tasks remains challenging.<n>This thesis proposes a series of methods to better adapt LMs to downstream applications.
  arXiv  Detail & Related papers  (2025-06-26T00:49:35Z)
• ToolACE-R: Tool Learning with Adaptive Self-Refinement [84.69651852838794]
  Tool learning allows Large Language Models to leverage external tools for solving complex user tasks. We propose ToolACE-R, a novel method that introduces adaptive self-refinement for tool invocations. Our results demonstrate the effectiveness of the proposed method, which is compatible with base models of various sizes.
  arXiv  Detail & Related papers  (2025-04-02T06:38:56Z)
• Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models [0.8356765961526956]
  Large Language Models (LLMs) have demonstrated remarkable in-context learning capabilities. This paper proposes a generative design method by leveraging the in-context learning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-03-28T13:10:04Z)
• LLM Post-Training: A Deep Dive into Reasoning Large Language Models [131.10969986056]
  Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations.
  arXiv  Detail & Related papers  (2025-02-28T18:59:54Z)
• Large Language Models as Attribution Regularizers for Efficient Model Training [0.0]
  Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains. We introduce a novel yet straightforward method for incorporating LLM-generated global task feature attributions into the training process of smaller networks. Our approach yields superior performance in few-shot learning scenarios.
  arXiv  Detail & Related papers  (2025-02-27T16:55:18Z)
• Complex LLM Planning via Automated Heuristics Discovery [48.07520536415374]
  We consider enhancing large language models (LLMs) for complex planning tasks. We propose automated inferences discovery (AutoHD), a novel approach that enables LLMs to explicitly generate functions to guide-time search. Our proposed method requires no additional model training or finetuning--and the explicit definition of functions generated by the LLMs provides interpretability and insights into the reasoning process.
  arXiv  Detail & Related papers  (2025-02-26T16:52:31Z)
• Improving In-Context Learning with Small Language Model Ensembles [2.3499129784547654]
  In-context learning (ICL) is a cheap and efficient alternative but cannot match the accuracies of advanced methods. We present Ensemble SuperICL, a novel approach that enhances ICL by leveraging the expertise of multiple fine-tuned small language models (SLMs)
  arXiv  Detail & Related papers  (2024-10-29T09:02:37Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
  We introduce BloomWise, a new prompting technique, inspired by Bloom's taxonomy, to improve the performance of Large Language Models (LLMs) The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM. In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-10-05T09:27:52Z)
• Leveraging Large Language Models for Enhanced Process Model Comprehension [33.803742664323856]
  In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the interpretability of complex process models.
  arXiv  Detail & Related papers  (2024-08-08T13:12:46Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning [12.651588927599441]
  Instruction tuning aims to align large language models with open-domain instructions and human-preferred responses. We introduce Task-Aware Curriculum Planning for Instruction Refinement (TAPIR) to select instructions that are difficult for a student LLM to follow. To balance the student's capabilities, task distributions in training sets are adjusted with responses automatically refined according to their corresponding tasks.
  arXiv  Detail & Related papers  (2024-05-22T08:38:26Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition [10.072992621244042]
  We propose a novel method for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (Tactile-AIRL) To enhance the performance of reinforcement learning (RL), we introduce active inference, which integrates model-based techniques and intrinsic curiosity into the RL process. We demonstrate that our method achieves significantly high training efficiency in non-prehensile objects pushing tasks.
  arXiv  Detail & Related papers  (2023-11-19T10:19:22Z)
• OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
  We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs. Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
  arXiv  Detail & Related papers  (2023-05-24T10:08:04Z)
• Self-Imitation Learning by Planning [3.996275177789895]
  Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge. In long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data. We propose self-imitation learning by planning (SILP), where demonstration data are collected automatically by planning on the visited states from the current policy. SILP is inspired by the observation that successfully visited states in the early reinforcement learning stage are collision-free nodes in the graph-search based motion planner.
  arXiv  Detail & Related papers  (2021-03-25T13:28:38Z)
• Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting [66.45372974713189]
  We propose a recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks. Experiments show that our method achieves state-of-the-art performance on the GLUE benchmark. We provide open-source RecAdam, which integrates the proposed mechanisms into Adam to facility the NLP community.
  arXiv  Detail & Related papers  (2020-04-27T08:59:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.