Papers with GCR
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)
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| Challenge: | Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks. |
| Approach: | They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs. |
| Outcome: | The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations. |
Mitigating Demonstration Bias through Global Coevolutionary Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for chain-of-thought prompting rely on manual demonstrations . experimental results show that GCR outperforms baseline methods without performance degradation . |
| Approach: | They propose a method that uses random samples to generate demonstrations in zero-shot settings. |
| Outcome: | The proposed method outperforms baseline methods on ten datasets without demonstration bias. |
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks without the need for any fine-tuning. |
| Approach: | They propose a method that diversifies the LLM generations while preserving their quality. |
| Outcome: | The proposed method can be used as training data to improve diversity in existing commonsense generators. |
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning (2025.emnlp-main)
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Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang
| Challenge: | Existing vision-language planning methods struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes. |
| Approach: | They propose a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training. |
| Outcome: | The proposed framework outperforms existing methods on short-horizon tasks but struggles with long-horizon reasoning in dynamic environments. |
Divergent Thinking: Escape the Homogeneity Trap in Generative Commonsense Reasoning (2026.findings-acl)
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| Challenge: | Generative commonsense reasoning requires models to synthesize coherent narratives that satisfy lexical constraints and commonsensical logic. |
| Approach: | They propose a framework that allows for deep semantic diversity rather than surface-level lexical variation. |
| Outcome: | The proposed framework achieves over 10% improvement in overall accuracy on NoRa and SPICE score on CommonGen-Lite. |
Synthetic Data Generation for Training Diversified Commonsense Reasoning Models (2026.acl-long)
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| Challenge: | Existing Generative Commonsense Reasoning datasets are created using a small number of human annotators, covering only a narrow set of commonsense scenarios. |
| Approach: | They propose to use a synthetic dataset to train diverse commonsense generators. |
| Outcome: | The proposed model improves both generation diversity and quality compared with vanilla models and human-crafted datasets across different size Large Language Models (LLMs). |