Papers by Yuxian Gu
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
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| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
| Outcome: | The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL. |
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)
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| Challenge: | Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization. |
| Approach: | They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks. |
| Outcome: | The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks. |
Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)
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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang
| Challenge: | Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability. |
| Approach: | They propose an off-policy influence estimation method that approximates data influence using offline trajectories. |
| Outcome: | The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency. |
PPT: Pre-trained Prompt Tuning for Few-shot Learning (2022.acl-long)
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| Challenge: | Prompt tuning for pre-trained language models has shown remarkable performance . however, prompt tuning is still not fully explored . |
| Approach: | They propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. |
| Outcome: | The proposed framework outperforms full-model tuning under full-data and few-shot learning settings. |
Train No Evil: Selective Masking for Task-Guided Pre-Training (2020.emnlp-main)
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| Challenge: | Pre-trained language models can't capture domain-specific and task-specific patterns because of the task-agnostic pre-training stage. |
| Approach: | They propose a task-guided pre-training stage with selective masking between general pre-train and fine-tuning to learn domain-specific patterns. |
| Outcome: | The proposed method can achieve comparable or even better performance with less than 50% of computation cost. |
Pre-Training to Learn in Context (2023.acl-long)
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| Challenge: | Pre-trained language models are not explicitly trained to learn in context. |
| Approach: | They propose a framework to enhance in-context learning by pre-training language models on a large collection of "intrinsic tasks" they evaluate the in-constitution learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark . |
| Outcome: | The proposed framework outperforms larger language models with nearly 4x parameters on seven widely-used datasets and the Super-NaturalInstrctions benchmark. |
Instruction Pre-Training: Language Models are Supervised Multitask Learners (2024.emnlp-main)
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| Challenge: | Unsupervised multitask pre-training has been the key to the success of language models (LMs) however, scaling it in the post-training stage trends towards better generalization. |
| Approach: | They propose a framework that augments massive raw corpora with instruction-response pairs to pre-train LMs. |
| Outcome: | The proposed framework augments massive raw corpora with instruction-response pairs to pre-train LMs. |