Papers with learning difficulty
Why Machine Reading Comprehension Models Learn Shortcuts? (2021.findings-acl)
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| Challenge: | Existing studies show that many MRC models learn shortcuts to outwit benchmarks, but the performance is unsatisfactory in real-world applications. |
| Approach: | They propose to use shortcut questions to analyze learning difficulty of MRC models . they propose to analyze the learning difficulty regarding shortcut and challenging questions . |
| Outcome: | The proposed methods show that a large proportion of shortcut questions in training data make models rely on shortcut tricks excessively. |
DSM: Question Generation over Knowledge Base via Modeling Diverse Subgraphs with Meta-learner (2022.emnlp-main)
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| Challenge: | Existing methods on knowledge base question generation learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraph. |
| Approach: | They propose a graph contrastive learning-based retriever to model diverse subgraphs with meta-learner to learn semantics-specific and semantics agnostic knowledge on and across these tasks. |
| Outcome: | The proposed approach reduces learning difficulty and improves performance on two widely-adopted benchmarks on KBQG. |
What Do LLMs Learn First? Asymmetric Learning Dynamics of Input Complexity and Output Ambiguity in Preference Alignment (2026.acl-long)
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| Challenge: | Existing methods treat all preference pairs uniformly during training. |
| Approach: | They propose a training framework that maintains separate, adaptive pacing schedules for each dimension. |
| Outcome: | The proposed training framework outperforms curriculum baselines by 2.1% and 0.21 points . it achieves 42.3% length-controlled win rate on AlpacaEval 2.0 and 7.66 on MT-Bench . |
Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System (2026.acl-long)
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| Challenge: | Vision-Language-Action models ground high-level semantic instructions into executable physical actions. |
| Approach: | They propose a Coarse-to-Fine Dual-System VLA architecture that decouples learning complexity into a coarse-to fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy. |
| Outcome: | The proposed architecture decouples learning complexity into a coarse-to-fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy. |