Papers by Jiacheng Ye
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing KG evaluation metrics are only aware of the exact correctness of predictions on phrase-level and ignore semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. |
| Approach: | They propose a fine-grained evaluation metric to improve the previous KG framework . the evaluation metrics are only aware of the exact correctness of predictions on phrase-level . |
| Outcome: | The proposed method outperforms the existing frameworks among all evaluation scores. |
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning is a common practice to randomly sample examples to serve as context. |
| Approach: | They propose a new principle for in-context learning that helps each sample find an in-constitut example organization that can derive the correct prediction. |
| Outcome: | The proposed method achieves 40% relative improvement over the common practice setting. |
Heterogeneous Graph Neural Networks for Keyphrase Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches for keyphrase generation generate uncontrollable and inaccurate absent keyphrases. |
| Approach: | They propose a graph-based method that captures explicit knowledge from related references. |
| Outcome: | The proposed model improves on baseline keyphrase generation models on multiple benchmarks. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
Generating Data for Symbolic Language with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) bring performance and complexity, but they incur a large computational cost in practice. |
| Approach: | They propose a task-based model which uses large language models to generate symbolic language data by an informative prompt and agreement-based verifier. |
| Outcome: | The proposed model can generate symbolic language data with a few human demonstrations and saves a considerable amount of inference effort. |
EEL: Efficiently Encoding Lattices for Reranking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for decoding conditional text are slow to apply to large numbers of hypotheses. |
| Approach: | They propose a method that can efficiently encode lattices of generated outputs using Transformers. |
| Outcome: | The proposed method can extract high-quality hypotheses from lattices with minimal degradation error compared to naive reranking methods. |
Uncertainty-Aware Label Refinement for Sequence Labeling (2020.emnlp-main)
Copied to clipboard
| Challenge: | Conditional random fields (CRF) for label decoding have been a problem for many tasks. |
| Approach: | They propose a two-stage label decoding framework that model long-term label dependencies while being much more computationally efficient. |
| Outcome: | The proposed method outperforms the CRF-based methods and greatly accelerates the inference process. |
Data to Defense: The Role of Curation in Aligning Large Language Models Against Safety Compromise (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have identified a vulnerability in large language models (LLMs) during customization. |
| Approach: | They propose an adaptive data curation approach that allows any text to be curated to enhance its effectiveness in counteracting harmful samples during customization. |
| Outcome: | The proposed approach reduces compromising effects and generates 100% safe responses. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
Copied to clipboard
Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
OpenICL: An Open-Source Framework for In-context Learning (2023.acl-demo)
Copied to clipboard
| Challenge: | In-context Learning (ICL) is a new paradigm for large language model evaluation. |
| Approach: | They propose an open-source toolkit for ICL and LLM evaluation. |
| Outcome: | The proposed framework is highly flexible and flexible and can be easily combined with other tools to suit users' needs. |
PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA (2024.acl-long)
Copied to clipboard
| Challenge: | Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA) is an intra-layer sharing mechanism that circumvents the drawbacks of peer parameter-sharing methods. |
| Approach: | They propose a partially rotation-enhanced low-rank adaptation (PRoLoRA) that shares four components to reduce the cost of LoRA and improves model capacity. |
| Outcome: | Empirical results show that PRoLoRA outperforms LoRA on multiple instruction tuning datasets. |
One2Set: Generating Diverse Keyphrases as a Set (2021.acl-long)
Copied to clipboard
| Challenge: | Recent keyphrase generation models are wrongly imposing a predefined order on keyphrases . a new training paradigm is proposed to concatenate keyphrase sequences in parallel . |
| Approach: | They propose a training paradigm that concatenates keyphrases in a predefined order . they propose combining a fixed set of learned control codes with a bipartite matching mechanism . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple benchmarks. |
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language models (PLMs). |
| Approach: | They propose a progressive zero-shot dataset generation framework which leverages feedback from the task-specific model to guide the generation of new training data via in-context examples. |
| Outcome: | The proposed framework achieves on-par or superior performance with only 1% synthetic dataset size, when compared to baseline methods without in-context feedback. |