Multi-Task Learning
1098 papers with code • 6 benchmarks • 55 datasets
Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.
( Image credit: Cross-stitch Networks for Multi-task Learning )
Libraries
Use these libraries to find Multi-Task Learning models and implementations
Cityscapes
CelebA
NYUv2
Clotho
ETHICS
KP20k
Hypersim
CELEX
Latest papers
Narrative Action Evaluation with Prompt-Guided Multimodal Interaction
NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor.
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA based Mixture of Experts
Unlike other LoRA based MoE methods, MixLoRA enhances model performance by utilizing independently configurable attention-layer LoRA adapters, supporting the use of LoRA and its variants for the construction of experts, and applying auxiliary load balance loss to address the imbalance problem of the router.
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency
easonsi/ssene
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To achieve NTE, we devise a novel Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is built based on a generative pretrained language model (PLM) {of Encoder-Decoder architecture} with a multi-task learning framework.
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts
Large language models like ChatGPT have shown substantial progress in natural language understanding and generation, proving valuable across various disciplines, including the medical field.
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies
With the surge of ChatGPT, the use of large models has significantly increased, rapidly rising to prominence across the industry and sweeping across the internet.
Multi-Task Learning for Features Extraction in Financial Annual Reports
For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information.
IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
exploration-lab/iitk-semeval-2024-task-4-pursuasion-techniques
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Memes are one of the most popular types of content used in an online disinformation campaign.
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes
harmonbhasin/curriculum_learning_icl
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Large language models (LLM) have recently shown the extraordinary ability to perform unseen tasks based on few-shot examples provided as text, also known as in-context learning (ICL).
Multi-Granularity Guided Fusion-in-Decoder
eunseongc/mgfid
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In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results.
Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages
In the on-device scenario (tiny and not pretrained SLU), our method improved the Overall Accuracy from 5. 31% to 22. 06% over the baseline Global-Local Contrastive Learning Framework (GL-CLeF) method.
