Papers with IL
Learning How to Actively Learn: A Deep Imitation Learning Approach (P18-1)
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| Challenge: | Experimental results show that heuristic-based active learning methods are limited when the data distribution of the underlying learning problems vary. |
| Approach: | They propose a method that learns an AL "policy" using "imitation learning" they use an efficient "algorithmic expert" which provides the policy learner with good actions in the encountered AL situations. |
| Outcome: | The proposed method is more effective than previous methods on two tasks . labeled data is rare while unlabelled data is abundant . |
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods to generate paraphrases are not trivial and often fail in practice. |
| Approach: | They propose to use imitation learning to boost the performance of generating paraphrases by using a pointer-generator model. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the benchmark datasets. |
Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue (2022.emnlp-main)
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| Challenge: | Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. |
| Approach: | They argue that imitation learning and low-level metrics are misleading . they compare existing models with IL and argue evaluation should focus on higher-level semantic goals . |
| Outcome: | The proposed model evaluations are based on three models and compare them with benchmarks . they show that existing models fail to ground query utterances, which are essential for task completion . |
GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning (2026.findings-acl)
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Yongyi Liao, Wencan Lai, Jun Fang, Jinjin Guo, Xiaohui Zhang, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Qixia Jiang
| Challenge: | Large language models demonstrate remarkable zero-shot generalization, but adapting to downstream tasks requires continual fine-tuning. |
| Approach: | They propose a method that incrementally constructs a pool of frozen, task-specific LoRA experts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in task-free and blurred-boundary settings. |