Transferring Procedural Knowledge across Commonsense Tasks

• URL: http://arxiv.org/abs/2304.13867v3
• Date: Mon, 20 Nov 2023 04:09:25 GMT
• Title: Transferring Procedural Knowledge across Commonsense Tasks
• Authors: Yifan Jiang, Filip Ilievski, Kaixin Ma
• Abstract summary: We study the ability of AI models to transfer procedural knowledge to novel narrative tasks in a transparent manner. We design LEAP: a comprehensive framework that integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of different architectures, training regimes, and augmentation strategies.
• Score: 17.929737518694616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI has no mechanisms to automatically track and explain procedures in unseen stories. To bridge this gap, we study the ability of AI models to transfer procedural knowledge to novel narrative tasks in a transparent manner. We design LEAP: a comprehensive framework that integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies based on both natural and synthetic stories. To address the lack of densely annotated training data, we devise a robust automatic labeler based on few-shot prompting to enhance the augmented data. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of different architectures, training regimes, and augmentation strategies. LEAP's labeler has a clear positive impact on out-of-domain datasets, while the resulting dense annotation provides native explainability.

Related papers

• Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation [6.324290412766366]
  skill library framework based on knowledge graphs endows robots with high-level skill awareness and spatial semantic understanding. At the motion level, an adaptive trajectory transfer method is developed using the A* algorithm and the skill library. At the physical level, we introduce an adaptive contour extraction and posture perception method based on tactile perception.
  arXiv  Detail & Related papers  (2024-11-18T16:42:07Z)
• Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning [70.64617500380287]
  Continual learning allows models to learn from new data while retaining previously learned knowledge. The semantic knowledge available in the label information of the images, offers important semantic information that can be related with previously acquired knowledge of semantic classes. We propose integrating semantic guidance within and across tasks by capturing semantic similarity using text embeddings.
  arXiv  Detail & Related papers  (2024-08-02T07:51:44Z)
• Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [79.28821338925947]
  Domain-Class Incremental Learning is a realistic but challenging continual learning scenario. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. This incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy overhead. We propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of
  arXiv  Detail & Related papers  (2024-07-07T12:19:37Z)
• AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents [19.249596397679856]
  AriGraph is a memory graph that integrates semantic and episodic memories while exploring the environment. We demonstrate that our Ariadne LLM agent effectively handles complex tasks within interactive text game environments difficult even for human players.
  arXiv  Detail & Related papers  (2024-07-05T09:06:47Z)
• Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos [16.333295670635557]
  We explore the capability of an agent to construct a logical sequence of action steps, thereby assembling a strategic procedural plan. This plan is crucial for navigating from an initial visual observation to a target visual outcome, as depicted in real-life instructional videos. We coin our approach KEPP, a novel Knowledge-Enhanced Procedure Planning system, which harnesses a probabilistic procedural knowledge graph extracted from training data.
  arXiv  Detail & Related papers  (2024-03-05T08:55:51Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• ArK: Augmented Reality with Knowledge Interactive Emergent Ability [115.72679420999535]
  We develop an infinite agent that learns to transfer knowledge memory from general foundation models to novel domains. The heart of our approach is an emerging mechanism, dubbed Augmented Reality with Knowledge Inference Interaction (ArK) We show that our ArK approach, combined with large foundation models, significantly improves the quality of generated 2D/3D scenes.
  arXiv  Detail & Related papers  (2023-05-01T17:57:01Z)
• Knowledge-Aware Procedural Text Understanding with Multi-Stage Training [110.93934567725826]
  We focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process. Two challenges, the difficulty of commonsense reasoning and data insufficiency, still remain unsolved. We propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which effectively leverages multiple forms of external knowledge.
  arXiv  Detail & Related papers  (2020-09-28T10:28:40Z)
• Automated Relational Meta-learning [95.02216511235191]
  We propose an automated relational meta-learning framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-01-03T07:02:25Z)

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.