MuLD: The Multitask Long Document Benchmark

• URL: http://arxiv.org/abs/2202.07362v1
• Date: Tue, 15 Feb 2022 12:42:55 GMT
• Title: MuLD: The Multitask Long Document Benchmark
• Authors: G Thomas Hudson, Noura Al Moubayed
• Abstract summary: We present a new long document benchmark consisting of only documents over 10,000 tokens. We show that models with increased context length are better able to solve the tasks presented.
• Score: 4.835289158553091
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficient techniques for processing much longer inputs. In this paper, we present MuLD: a new long document benchmark consisting of only documents over 10,000 tokens. By modifying existing NLP tasks, we create a diverse benchmark which requires models to successfully model long-term dependencies in the text. We evaluate how existing models perform, and find that our benchmark is much more challenging than their `short document' equivalents. Furthermore, by evaluating both regular and efficient transformers, we show that models with increased context length are better able to solve the tasks presented, suggesting that future improvements in these models are vital for solving similar long document problems. We release the data and code for baselines to encourage further research on efficient NLP models.

Related papers

• HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly [34.205934899868346]
  We present HELMET, a comprehensive benchmark encompassing seven diverse, application-centric categories. We find that synthetic tasks like NIAH are not good predictors of downstream performance. While most LCLMs achieve perfect NIAH scores, open-source models significantly lag behind closed ones when the task requires full-context reasoning.
  arXiv  Detail & Related papers  (2024-10-03T17:20:11Z)
• MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking [0.283600654802951]
  We present a summarization model designed to generate claim-specific summaries useful for fact-checking from multimodal datasets. We introduce a dynamic perceiver-based model that can handle inputs from multiple modalities of arbitrary lengths. Our approach outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset.
  arXiv  Detail & Related papers  (2024-07-18T01:33:20Z)
• KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches [52.02764371205856]
  Long context capability is a crucial competency for large language models (LLMs) This work provides a taxonomy of current methods and evaluating 10+ state-of-the-art approaches across seven categories of long context tasks.
  arXiv  Detail & Related papers  (2024-07-01T17:59:47Z)
• On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval [59.25292920967197]
  Few-shot document entity retrieval (VDER) is an important topic in industrial NLP applications. FewVEX is a new dataset to boost future research in the field of entity-level few-shot VDER. We present a task-aware meta-learning based framework, with a central focus on achieving effective task personalization.
  arXiv  Detail & Related papers  (2023-11-01T17:51:43Z)
• Fast and Accurate Factual Inconsistency Detection Over Long Documents [19.86348214462828]
  We introduce SCALE, a task-agnostic model for detecting factual inconsistencies using a novel chunking strategy. This approach achieves state-of-the-art performance in factual inconsistency detection for diverse tasks and long inputs. We have released our code and data publicly to GitHub.
  arXiv  Detail & Related papers  (2023-10-19T22:55:39Z)
• Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering [49.85790367128085]
  We pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. This novel multi-document QA formulation directs the model to better recover cross-text informational relations. Unlike prior multi-document models that focus on either classification or summarization tasks, our pre-training objective formulation enables the model to perform tasks that involve both short text generation and long text generation.
  arXiv  Detail & Related papers  (2023-05-24T17:48:40Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
  We propose a principled inference framework to improve summarization models on two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
  arXiv  Detail & Related papers  (2022-03-15T01:24:51Z)
• LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation [49.57366550980932]
  Long text modeling requires many capabilities such as modeling long-range commonsense and discourse relations. We propose LOT, a benchmark including two understanding and two generation tasks for Chinese long text modeling evaluation. We release an encoder-decoder Chinese long text pretraining model named LongLM with up to 1 billion parameters.
  arXiv  Detail & Related papers  (2021-08-30T02:38:32Z)
• Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching [28.190001111358438]
  We propose a Siamese Multi-depth Transformer-based SMITH for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input. We will open source a Wikipedia based benchmark dataset, code and a pre-trained checkpoint to accelerate future research on long-form document matching.
  arXiv  Detail & Related papers  (2020-04-26T07:04:08Z)

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.