Papers by Aston Zhang
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives (P19-1)
Copied to clipboard
Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
| Challenge: | Using a pointer-generator framework for reading/sampling over large documents, we propose a framework for learning over long narratives where documents easily span over thousands of tokens. |
| Approach: | They propose a curriculum learning (CL) based pointer-generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. |
| Outcome: | The proposed framework improves on the NarrativeQA reading comprehension benchmark and reaches state-of-the-art performance. |
Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in scale have enabled large language models to perform NLP tasks zero-shot . however, it is not known whether ChatGPT can serve as a generalist model that can perform many NLP jobs zero- shot. |
| Approach: | They empirically evaluate ChatGPT's zero-shot learning ability on 20 popular NLP datasets . they find it performs well on many tasks favoring reasoning abilities . |
| Outcome: | The proposed model can perform many NLP tasks zero-shot without adaptation on downstream data. |
Dive into Deep Learning for Natural Language Processing (D19-2)
Copied to clipboard
| Challenge: | GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP. |
| Approach: | This hands-on tutorial demonstrates how to scale unsupervised pre-training techniques with Apache MXNet and GluonNLP. |
| Outcome: | This hands-on tutorial examines the challenges of scaling these models and algorithms effectively with Apache MXNet and GluonNLP. |
OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing efforts to build GUI agents focused on the autonomous mode have failed to address the problem of over-execution. |
| Approach: | They propose an adaptive GUI agent that predicts confidence levels at each interaction step and elicits adaptive interaction. |
| Outcome: | The proposed GUI agent outperforms existing models on a complex dataset and on established benchmarks. |
In-Context Learning with Iterative Demonstration Selection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample . Existing studies have shown that the optimal selection dimension, i.e., diversity or similarity, is task-specific. |
| Approach: | They propose to use zero-shot chain-of-thought reasoning to iteratively select examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. |
| Outcome: | The proposed method outperforms existing demonstration selection methods on reasoning, question answering, and topic classification tasks. |
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing diffusion models have limitations in modeling discrete data, e.g., languages . we present a novel diffusion model for language modeling inspired by linguistic features in languages based on iterative denoising . |
| Approach: | They propose a method that iteratively denoises and adds corruptions to the textual data through soft-masking to better noise it. |
| Outcome: | The proposed model achieves better generation quality and lower training cost than current models with better performance. |
On Orthogonality Constraints for Transformers (2021.acl-short)
Copied to clipboard
Aston Zhang, Alvin Chan, Yi Tay, Jie Fu, Shuohang Wang, Shuai Zhang, Huajie Shao, Shuochao Yao, Roy Ka-Wei Lee
| Challenge: | a dedicated study on orthogonality constraints for transformers has been lacking . plug-and-play constraints increase the BLEU of transformers . |
| Approach: | They propose to use plug-and-play constraints to encourage matrices to be orthogonal for numerical stability. |
| Outcome: | The proposed constraint increases the BLEU on the large-scale WMT’16 EnDe benchmark by a factor of 28.4 to 29.6. |
Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)
Copied to clipboard
Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou
| Challenge: | Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format. |
| Approach: | They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision. |
| Outcome: | The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges. |
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)
Copied to clipboard
| Challenge: | Existing models for natural language processing are heavily parameterized and memory inefficient. |
| Approach: | They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation . |
| Outcome: | The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance. |
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Using a poison signature, attackers can manipulate training data to manipulate the target class at test time. |
| Approach: | They propose a backdoor poisoning attack that generates poisoned training samples by poison injection in latent space and a conditional adversarially regularized autoencoder to generate poisones. |
| Outcome: | The proposed attack generates poisoned training samples by poison injection in latent space and shows that the target class can be steered to the poison class with success rates of >80% when the input hypothesis is injected with the poison signature. |
You Only Look at Screens: Multimodal Chain-of-Action Agents (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to creating autonomous graphical user interfaces rely on external tools and application-specific APIs to interpret the environment. |
| Approach: | They propose a multimodal solution that directly interacts with the user interface without environment parsing. |
| Outcome: | The proposed solution bypasses environment parsing and reliance on application-dependent APIs. |
Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions (2025.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that multimodal GUI agents are susceptible to environmental distractions. |
| Approach: | They propose a scenario where both user and agent are benign and environment is not malicious . they implement an adversarial environment injection and analyze the approach to improve faithfulness . |
| Outcome: | The proposed approach improves faithfulness of multimodal large language model agents in a graphical user interface environment. |
A Systematic Examination of Preference Learning through the Lens of Instruction-Following (2025.naacl-long)
Copied to clipboard
Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong, Rui Hou, Melanie Kambadur, Dhruv Mahajan, Hannaneh Hajishirzi, Liang Tan
| Challenge: | a recent study has found that preference learning is a key tool for enhancing LLM training and alignment. |
| Approach: | They use a synthetic data generation pipeline to generate 48,000 unique instruction-following prompts with 23 verifiable constraints to obtain preference pairs. |
| Outcome: | The proposed pipeline generates 48,000 unique instruction-following prompts with 23 verifiable constraints that enable fine-grained and automated quality assessments of model responses. |
Automated Few-Shot Classification with Instruction-Finetuned Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing few-shot learning approaches combine language models with prompts, but they often require domain knowledge and substantial guesswork. |
| Approach: | They propose a method to eliminate the need for handcrafted prompts by generating two distinct, semantically meaningful class descriptions and a selection mechanism via cross-validation. |
| Outcome: | The proposed method outperforms state-of-the-art few-shot learning methods over 12 datasets, spanning 8 classification tasks. |