Papers with task learning
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification (2020.coling-main)
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| Challenge: | Existing deep learning paradigms focus on learning a model from training data of a single task and the learned model is also tested on the same task. |
| Approach: | They propose a Bayes-enhanced lifelong attention network to learn attention knowledge from a sequence of sentiment classification tasks and build lifelong ones. |
| Outcome: | The proposed model is able to learn attention knowledge from a set of sentiment classification tasks and build lifelong attentions. |
Using the Past Knowledge to Improve Sentiment Classification (2020.findings-emnlp)
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| Challenge: | Existing model retains knowledge learned from past tasks and selectively transfers it to new task to help it learn better. |
| Approach: | They propose a lifelong learning model that can retain and selectively transfer the knowledge learned in the past to help learn the new task. |
| Outcome: | The proposed model outperforms strong baselines, including even multiple task learning. |
GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics (2024.findings-acl)
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| Challenge: | Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios. |
| Approach: | They propose to use class-wise hardness to measure class-specific properties of data in the semantic embedding space by modeling class geometry in the . semantic embeddining space. |
| Outcome: | The proposed method surpasses instance-level metrics by over 59 percent on Pearson‘s correlation on measuring class-wise hardness. |
Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring (2021.findings-acl)
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| Challenge: | despite recent progress, learning new tasks through language instructions remains a challenging problem. |
| Approach: | They propose a hierarchical task learning approach that decomposes task learning into three sub-problems and a model that addresses each sub-probability in a unified manner. |
| Outcome: | The proposed model achieves the state-of-the-art performance on the AL-FRED benchmark . it decomposes task learning into three sub-problems and addresses them in a unified manner . |
What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning (2023.findings-acl)
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| Challenge: | Large language models (LLMs) can perform in-context learning (ICL) with only a few demonstrations, but its mechanisms are not well-understood. |
| Approach: | They characterize two ways in which LLMs leverage demonstrations to solve tasks with a few demonstrations. |
| Outcome: | The proposed model achieves non-trivial performance with only TR, and TR does not improve with larger models or more demonstrations. |
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)
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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee
| Challenge: | Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components. |
| Approach: | They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. |
| Outcome: | The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility. |
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. |
Continual Safety Alignment via Gradient-Based Sample Selection (2026.findings-acl)
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| Challenge: | Large language models require continuous adaptation to new domains, tasks, and evolving requirements. |
| Approach: | They propose a gradient-based sample selection method that filters high-gradient samples during fine-tuning. |
| Outcome: | The proposed method significantly improves alignment preservation while maintaining competitive task performance on continual domain tasks. |
ICL CIPHERS: Quantifying ”Learning” in In-Context Learning via Substitution Ciphers (2025.emnlp-main)
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| Challenge: | Recent studies suggest that In-Context Learning operates in dual modes . however, disentangling these modes remains a challenging goal . |
| Approach: | They propose a class of task reformulations based on substitution ciphers borrowed from classic cryptography. |
| Outcome: | The proposed model can solve tasks with a BIJECTIVE mapping, but it requires 'deciphering' the latent cipher. |
Rendering Data Unlearnable by Exploiting LLM Alignment Mechanisms (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. |
| Approach: | They propose a data-level defence that renders text unlearnable to LLMs by injecting carefully designed alignment-triggering disclaimers into the models' alignment mechanisms. |
| Outcome: | The proposed approach exploits the models’ alignment mechanisms to prevent effective learning. |