Papers with Multi-task learning
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes (2024.naacl-short)
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| Challenge: | Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential. |
| Approach: | They propose to combine multi-task learning (MTL) with in-context learning (ICL) to build models that can generalize to multiple tasks while being robust to out-of-distribution examples. |
| Outcome: | The proposed training strategies enable models to learn difficult tasks while mixing in prior tasks, denoted as mixed curriculum. |
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks (2022.naacl-industry)
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Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi
| Challenge: | Multi-task learning (MTL) aims to solve multiple tasks by sharing a base representation among them. |
| Approach: | They propose an approach that allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule. |
| Outcome: | The proposed method outperforms existing methods in two 5-task MTL setups. |
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models (D19-61)
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| Challenge: | Multi-task learning and self-training are two common ways to improve a machine learning model’s performance in settings with limited training data. |
| Approach: | They propose a transductive auxiliary task self-training procedure that trains a model on auxiliary tasks and test instances with auxiliary labels generated by a single-task version of the model. |
| Outcome: | The proposed method improves accuracy by 9.56% over the pure multi-task model for dependency relation tagging and 13.03% for semantic taging. |
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods (2023.eacl-main)
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| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
| Approach: | They propose to summarize recent advances in multi-task learning methods based on their task relatedness into two general multi-step training methods. |
| Outcome: | The proposed methods summarize the tasks and discuss future directions. |
Multi-task Active Learning for Pre-trained Transformer-based Models (2022.tacl-1)
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| Challenge: | Multi-task learning requires annotating the same text with multiple annotation schemes, which can be costly and laborious. |
| Approach: | They propose to use multi-task active learning paradigm to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. |
| Outcome: | The proposed model minimizes annotation efforts for multi-task NLP models by iterating on the most valuable examples. |
Gated Multi-Task Network for Text Classification (N18-2)
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| Challenge: | Existing approaches to multitask learning share the features without distinguishing the usefulness of the features, generating undesired interference between tasks. |
| Approach: | They propose to introduce a gate mechanism into multi-task CNN and propose a new gated sharing unit which can filter the feature flows between tasks and greatly reduce the interference. |
| Outcome: | The proposed approach can learn selection rules automatically and gain a great improvement over strong baselines. |
Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)
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| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
| Approach: | They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks. |
| Outcome: | The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training. |
Soft Representation Learning for Sparse Transfer (P19-1)
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| Challenge: | Using adversarial training, we can “soft-code” shared and private spaces to avoid sparse sharing. |
| Approach: | They propose to use adversarial training to “soft-code” shared and private spaces to avoid the shared space gets too sparse. |
| Outcome: | The proposed architecture avoids sparse sharing of shared and private spaces, and also deals with low-quality input. |
Tricks for Training Sparse Translation Models (2022.naacl-main)
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| Challenge: | Multitask learning with an unbalanced data distribution skews model learning towards high resource tasks. |
| Approach: | They propose to use a temperature heating mechanism and dense pre-training to mitigate this by training models with a fixed model capacity. |
| Outcome: | The proposed techniques improve performance on two multilingual translation benchmarks compared to BASELayers and Dense scaling baselines and in combination, more than 2x model convergence speed. |
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (N19-1)
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| Challenge: | Multi-task learning (MTL) has been studied for sequence labeling tasks . auxiliary tasks are selected specifically to improve performance of a target task . |
| Approach: | They propose a shared-cell long-short-term memory cell which contains shared parameters that can learn from all tasks and task-specific parameters that could learn task-related information. |
| Outcome: | The proposed model can learn from all tasks and task-specific parameters. |
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks (2020.acl-main)
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| Challenge: | Multitask learning and transfer learning are techniques to overcome data scarcity . finding suitable auxiliary datasets for multitask learning is a trial-and-error approach . |
| Approach: | They propose to automatically assess the similarity of sequence tagging datasets to identify beneficial auxiliary data for MTL or TL setups. |
| Outcome: | The proposed methods can compute similarity between two sequence tagging datasets . they show that the same measures correlate with the change in test score of the auxiliary dataset . |
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (N19-1)
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| Challenge: | Multi-task learning is an inductive transfer mechanism that leverages information from related tasks to improve the primary model's generalization performance. |
| Approach: | They propose a multitask learning pipeline that finds relevant auxiliary tasks and learns their mixing ratio. |
| Outcome: | The proposed model can find relevant auxiliary tasks and learn their mixing ratio . the proposed model achieves significant performance boosts on several primary tasks . |
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP (2023.acl-long)
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| Challenge: | Multi-task learning (MTL) is a machine learning paradigm where multiple learning tasks are optimized simultaneously, exploiting commonalities and differences across them. |
| Approach: | They propose a parameter-efficient MTL architecture to improve task aggregation and to include loosely related skills from multiple datasets. |
| Outcome: | The proposed architecture outperforms single-task learning (STL) and is expected to outperformed it. |
The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders (2021.emnlp-main)
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| Challenge: | Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency. |
| Approach: | They propose a multi-task learning technique that uses transformer encoders to improve performance on closely-related tasks. |
| Outcome: | The proposed method performs better on five NLP tasks than single-task learning on similar tasks. |
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing multi-task learning approaches for large language models fall short due to computational intensive or lack of simultaneous task convergence. |
| Approach: | They propose a new multi-task learning approach that dynamically adjusts task weights during the training process, ensuring that the validation loss of all tasks progresses towards convergence at an even pace. |
| Outcome: | The proposed approach improves the performance of large language models by up to 13% compared to the second-best approaches. |
Low-Rank Updates of pre-trained Weights for Multi-Task Learning (2023.findings-acl)
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| Challenge: | Multi-task learning is a popular approach for learning with pre-trained models due to the complexity of the tasks and the challenges associated with fine-tuning large pre-train models. |
| Approach: | They propose a new approach for Multi-task learning which is based on stacking the weights of Neural Networks as a tensor. |
| Outcome: | The proposed approach achieves equivalent performance to the state-of-the-art on the general language understanding evaluation benchmark by training only 0.3 of the parameters per task while not modifying the baseline weights. |
Multi-Task Label Embedding for Text Classification (D18-1)
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| Challenge: | Existing work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. |
| Approach: | They propose to combine multi-task learning with semantic vectors to convert labels into vectors . their results are based on extensive experiments on five benchmark datasets based in chinese . |
| Outcome: | The proposed model can improve performance on five benchmark datasets on text classification tasks. |
MCapsNet: Capsule Network for Text with Multi-Task Learning (D18-1)
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| Challenge: | Multi-task learning has been frustrated by the interference among tasks. |
| Approach: | They propose a capsule-based multi-task learning architecture which is unified, simple and effective. |
| Outcome: | The proposed model can cluster features for each task in the network, which helps reduce the interference among tasks. |
Enhancing Image-to-Text Generation in Radiology Reports through Cross-modal Multi-Task Learning (2024.lrec-main)
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| Challenge: | Image-to-text generation relies on independent models for image understanding and natural language generation, which often exhibit a semantic gap between visual and textual information. |
| Approach: | They propose a multi-task learning framework to leverage both visual and non-imaging data for generating radiology reports. |
| Outcome: | The proposed framework improves performance over single-task baselines across language generation metrics and mitigates overfitting in auxiliary tasks. |
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale (2024.findings-acl)
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| Challenge: | Multi-task learning (MTL) has shown significant practical benefits when using language models . current two stage MTL introduces a substantial number of additional parameters . |
| Approach: | They propose a multi-task learning method that leverages existing knowledge for a target task. |
| Outcome: | The proposed method outperforms baselines on three benchmarks and two encoder LMs with a small number of transfer parameters. |
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models (2025.emnlp-main)
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Zhen Zhang, Yifan Yang, Kai Zhen, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann, Zheng Zhang
| Challenge: | Large language models (LLMs) have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning requires significant memory, posing challenges for resource-constrained environments. |
| Approach: | They propose a ZO-based framework that eliminates the need for backpropagation and provides a memory-efficient alternative to backprograming. |
| Outcome: | The proposed framework surpasses first-order methods in performance and accuracy. |
Impartial Multi-task Representation Learning via Variance-invariant Probabilistic Decoding (2025.acl-long)
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| Challenge: | Existing methods focus on balancing loss or gradients but fail to address this issue due to the representation discrepancy in latent space. |
| Approach: | They propose a framework that harmonizes representation spaces across tasks to ensure impartial learning by harmonizing representation spaces. |
| Outcome: | The proposed framework outperforms 12 representative methods under the same multi-task settings, especially in heterogeneous task combinations and data-constrained scenarios. |
A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding (2026.findings-acl)
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| Challenge: | a novel multi-task learning framework for domain-specific natural language understanding tasks addresses these limitations by combing multiple tasks into a single framework. |
| Approach: | They propose a multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. |
| Outcome: | The proposed framework surpasses conventional multi-task learning approaches in performance. |
Multi-Task Representation Alignment on Language Understanding: A Mutual Information Perspective (2026.acl-long)
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| Challenge: | Existing approaches to multitask learning fail to address task interference issues . Existing methods focus on task balancing or probabilistic modeling but fail to learn sufficient representations for all target tasks. |
| Approach: | They propose a multi-task representation alignment framework to achieve task-specific alignment and self-alignment on shared representations from a mutual information perspective. |
| Outcome: | The proposed framework outperforms 13 representative MTL methods under label-noisy and data-constrained conditions. |