Papers with HINT
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)
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| Challenge: | Text classification is a primary task in natural language processing (NLP). |
| Approach: | They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. |
Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)
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Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao
| Challenge: | Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training. |
| Approach: | They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity. |
| Outcome: | The proposed framework outperforms baseline models while maintaining high Affinity. |
HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation (2023.acl-long)
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| Challenge: | Recent NLP models have shown the remarkable ability to generalise ‘zero-shot’ to new tasks using only natural language instructions as guidance. |
| Approach: | They introduce Hypernetworks for INstruction Tuning (HINT) which converts task instructions and examples into parameter-efficient modules inserted into an underlying model using a pretrained text encoder. |
| Outcome: | The proposed models outperform strong state-of-the-art models by over 10% when controlling for compute. |