JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning (2025.findings-acl)
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| Challenge: | Existing text-to-text methods struggle with issues such as generalization, robustness, and controllability due to their lack of explicit task structures. |
| Approach: | They propose a structure-to-structure approach that uses JSON structures to represent tasks. |
| Outcome: | The proposed method outperforms TextTuning in terms of performance, robustness, and controllability across different scenarios. |
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
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| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
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| Challenge: | Recent work shows that Code Large Language Models can address a wide range of code-related tasks. |
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| Challenge: | Decoding methods are essential for converting language models from next-token predictors into practical task solvers. |
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
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| Challenge: | Pre-trained large language models retain task-specific knowledge, but where and to what extent they retain it remains unexplored. |
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