Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards

• URL: http://arxiv.org/abs/2108.07374v1
• Date: Mon, 16 Aug 2021 23:15:09 GMT
• Title: Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards
• Abstract summary: We present two case studies of efforts that aim to develop reusable documentation templates. We describe our process for developing these templates, including the identification of relevant stakeholder groups.
• Score: 11.251235763359862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NLP) tools. Nevertheless, the adoption of standard documentation practices across the field of NLP promotes more accessible and detailed descriptions of NLP datasets and models, while supporting researchers and developers in reflecting on their work. To help with the standardization of documentation, we present two case studies of efforts that aim to develop reusable documentation templates -- the HuggingFace data card, a general purpose card for datasets in NLP, and the GEM benchmark data and model cards with a focus on natural language generation. We describe our process for developing these templates, including the identification of relevant stakeholder groups, the definition of a set of guiding principles, the use of existing templates as our foundation, and iterative revisions based on feedback.

Related papers

• When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs [64.27273946787344]
  Recent Long-Context Language Models can process hundreds of thousands of tokens in a single prompt.<n>We recast reasoning as reusable thought caches, derived from prior problem solving traces.<n>We propose an update strategy that iteratively refines templates derived from training data through natural-language feedback.
  arXiv  Detail & Related papers  (2025-10-08T19:52:35Z)
• Detection and Measurement of Syntactic Templates in Generated Text [58.111650675717414]
  We offer an analysis of syntactic features to characterize general repetition in models. We find that models tend to produce templated text in downstream tasks at a higher rate than what is found in human-reference texts.
  arXiv  Detail & Related papers  (2024-06-28T19:34:23Z)
• Visually Guided Generative Text-Layout Pre-training for Document Intelligence [51.09853181377696]
  We propose visually guided generative text-pre-training, named ViTLP. Given a document image, the model optimize hierarchical language and layout modeling objectives to generate the interleaved text and layout sequence. ViTLP can function as a native OCR model to localize and recognize texts of document images.
  arXiv  Detail & Related papers  (2024-03-25T08:00:43Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• GPT Struct Me: Probing GPT Models on Narrative Entity Extraction [2.049592435988883]
  We evaluate the capabilities of two state-of-the-art language models -- GPT-3 and GPT-3.5 -- in the extraction of narrative entities. This study is conducted on the Text2Story Lusa dataset, a collection of 119 Portuguese news articles.
  arXiv  Detail & Related papers  (2023-11-24T16:19:04Z)
• Instruct and Extract: Instruction Tuning for On-Demand Information Extraction [86.29491354355356]
  On-Demand Information Extraction aims to fulfill the personalized demands of real-world users. We present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE.
  arXiv  Detail & Related papers  (2023-10-24T17:54:25Z)
• Probing Representations for Document-level Event Extraction [30.523959637364484]
  This work is the first to apply the probing paradigm to representations learned for document-level information extraction. We designed eight embedding probes to analyze surface, semantic, and event-understanding capabilities relevant to document-level event extraction. We found that trained encoders from these models yield embeddings that can modestly improve argument detections and labeling but only slightly enhance event-level tasks.
  arXiv  Detail & Related papers  (2023-10-23T19:33:04Z)
• Total Error Sheets for Datasets (TES-D) -- A Critical Guide to Documenting Online Platform Datasets [0.0]
  This paper proposes a template for documenting datasets that have been collected from online platforms for research purposes. The template should help to critically reflect on data quality and increase transparency in research fields that make use of online platform data.
  arXiv  Detail & Related papers  (2023-06-25T12:00:09Z)
• Key Information Extraction From Documents: Evaluation And Generator [3.878105750489656]
  This research project compares state-of-the-art models for information extraction from documents. The results have shown that NLP based pre-processing is beneficial for model performance. The use of a bounding box regression decoder increases the model performance only for fields that do not follow a rectangular shape.
  arXiv  Detail & Related papers  (2021-06-09T16:12:21Z)
• GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction [134.5580003327839]
  We introduce a generative transformer-based encoder-decoder framework (GRIT) to model context at the document level. We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work.
  arXiv  Detail & Related papers  (2020-08-21T01:07:36Z)
• SPECTER: Document-level Representation Learning using Citation-informed Transformers [51.048515757909215]
  SPECTER generates document-level embedding of scientific documents based on pretraining a Transformer language model. We introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction to document classification and recommendation.
  arXiv  Detail & Related papers  (2020-04-15T16:05:51Z)

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.