Learning Action Conditions from Instructional Manuals for Instruction Understanding

• URL: http://arxiv.org/abs/2205.12420v2
• Date: Wed, 3 Jul 2024 00:49:28 GMT
• Title: Learning Action Conditions from Instructional Manuals for Instruction Understanding
• Authors: Te-Lin Wu, Caiqi Zhang, Qingyuan Hu, Alex Spangher, Nanyun Peng,
• Abstract summary: We propose a task dubbed action condition inference, and collecting a high-quality, human annotated dataset of preconditions and postconditions of actions in instructional manuals. We propose a weakly supervised approach to automatically construct large-scale training instances from online instructional manuals, and curate a densely human-annotated and validated dataset to study how well the current NLP models can infer action-condition dependencies in instruction texts.
• Score: 48.52663250368341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to infer pre- and postconditions of an action is vital for comprehending complex instructions, and is essential for applications such as autonomous instruction-guided agents and assistive AI that supports humans to perform physical tasks. In this work, we propose a task dubbed action condition inference, and collecting a high-quality, human annotated dataset of preconditions and postconditions of actions in instructional manuals. We propose a weakly supervised approach to automatically construct large-scale training instances from online instructional manuals, and curate a densely human-annotated and validated dataset to study how well the current NLP models can infer action-condition dependencies in the instruction texts. We design two types of models differ by whether contextualized and global information is leveraged, as well as various combinations of heuristics to construct the weak supervisions. Our experimental results show a >20% F1-score improvement with considering the entire instruction contexts and a >6% F1-score benefit with the proposed heuristics.

Related papers

• Instruction Embedding: Latent Representations of Instructions Towards Task Identification [20.327984896070053]
  For instructional data, the most important aspect is the task it represents, rather than the specific semantics and knowledge information. In this work, we introduce a new concept, instruction embedding, and construct Instruction Embedding Benchmark (IEB) for its training and evaluation.
  arXiv  Detail & Related papers  (2024-09-29T12:12:24Z)
• From Instance Training to Instruction Learning: Task Adapters Generation from Instructions [29.452006810725184]
  This paper focuses on simulating human learning to address the shortcomings of instance training. We introduce Task Adapters Generation from Instructions (TAGI), which automatically constructs the task-specific model. We evaluate TAGI on the Super-Natural Instructions and P3 datasets.
  arXiv  Detail & Related papers  (2024-06-18T08:14:28Z)
• Instruction Diversity Drives Generalization To Unseen Tasks [1.9059113568275998]
  Generalization emerges once a diverse enough set of tasks is provided, even though very few examples are provided for each task. Generalization emerges once a diverse enough set of tasks is provided, even though very few examples are provided for each task.
  arXiv  Detail & Related papers  (2024-02-16T18:47:21Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• One-Shot Learning as Instruction Data Prospector for Large Language Models [108.81681547472138]
  textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets. We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
  arXiv  Detail & Related papers  (2023-12-16T03:33:12Z)
• Auto-ICL: In-Context Learning without Human Supervision [93.05202223767463]
  We propose Automatic In-Context Learning framework that enables the model to autonomously generate examples and instructions for problem-solving. With experiments across various models and datasets, results show that model-generated contexts outperform human-annotated contexts.
  arXiv  Detail & Related papers  (2023-11-15T07:37:28Z)
• Instruction Tuning with Human Curriculum [15.025867460765559]
  We introduce Curriculum Instruction Tuning, (2) explore the potential advantages of employing diverse curriculum strategies, and (3) delineate a synthetic instruction-response generation framework. Our generation pipeline is systematically structured to emulate the sequential and orderly characteristic of human learning. We describe a methodology for generating instruction-response datasets that extensively span the various stages of human education.
  arXiv  Detail & Related papers  (2023-10-14T07:16:08Z)
• Instruction Position Matters in Sequence Generation with Large Language Models [67.87516654892343]
  Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization. We propose enhancing the instruction-following capability of LLMs by shifting the position of task instructions after the input sentences.
  arXiv  Detail & Related papers  (2023-08-23T12:36:57Z)
• Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning [74.70157466822612]
  We systematically study the role of task definitions in instruction learning. We find that model performance drops substantially when removing contents describing the task output. We propose two strategies to help models better leverage task instructions.
  arXiv  Detail & Related papers  (2023-06-01T21:11:24Z)

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.