Papers by Zhen Qin
SciPedia: Unlocking the Value of Scientific Data for Pre-training (2026.acl-long)
Copied to clipboard
| Challenge: | High-quality scientific data is critical for advancing LLMs, yet academic literature remains underutilized. |
| Approach: | They construct a large-scale raw scientific corpus but identify a critical Learnability Gap . they develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation . |
| Outcome: | The proposed approach boosts average performance by +2.12 (3B) and +2.95 (7B) on in-domain tasks. |
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to generate questions based on answers and relevant contexts are not suitable for all questions . |
| Approach: | They propose a method to generate questions from a given answer and its relevant context. |
| Outcome: | The proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches. |
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)
Copied to clipboard
Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang
| Challenge: | Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed . |
| Approach: | They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management. |
| Outcome: | The proposed system automates human management by using a collaborative multi-agent system. |
ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models face intrinsic limitations in coding with unseen APIs in training corpora. |
| Approach: | They propose a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by planning a complex problem into several API invocation subtasks and experimenting with correct API usage at intermediate steps. |
| Outcome: | The proposed framework significantly improves performance for models lacking prior API knowledge, achieving 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10. |
Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing adversarial attacks using imperceptible perturbations are challenging to simulate . e-commerce product restrictions and hate speech monitoring are examples of such attacks . |
| Approach: | They propose a black-box adversarial attack that leverages sparse perturbations to simulate adversarials exhibited by illegal merchants in the black- box scenario. |
| Outcome: | The proposed method outperforms existing attacks and unimodal attacks by treating images and text in discrete space and outperforming existing models. |
Predicting Text Preference Via Structured Comparative Reasoning (2024.acl-long)
Copied to clipboard
Jing Nathan Yan, Tianqi Liu, Justin Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
| Challenge: | Existing approaches to comparative reasoning rely on pretraining or fine-tuning models at the cost of massive human annotation and computation. |
| Approach: | They propose a model that prompts LLMs to generate structured intermediate comparisons by proposing aspects for comparison, followed by generating textual comparisons under each aspect. |
| Outcome: | The proposed model significantly reduces hallucination and improves consistency across various NLP tasks. |
Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels (2024.naacl-short)
Copied to clipboard
| Challenge: | Existing pointwise LLMs provide noisy or biased answers for documents that are partially relevant to the query. |
| Approach: | They propose to incorporate fine-grained relevance labels into the LLM prompt . they propose to better differentiate between documents with different levels of relevance . |
| Outcome: | The proposed model can differentiate between documents with different levels of relevance to the query and derive a more accurate ranking. |
Multilingual Fine-Grained News Headline Hallucination Detection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models to generate news headlines often suffer from the "hallucination" problem, where the produced headline does not fully align with the source article's content. |
| Approach: | They propose to use a multilingual, fine-grained dataset to detect news headlines in 5 languages using supervised fine-tuning techniques and coarse-to-fine prompting to boost the few-shot detection performance. |
| Outcome: | The proposed methods boost the few-shot hallucination detection performance in terms of the example-F1 metric. |
AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)
Copied to clipboard
Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng
| Challenge: | Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content. |
| Approach: | They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories . |
| Outcome: | The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources. |
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate relevance labels for large language models have not been successful in generating relevance labels. |
| Approach: | They propose a method to combine LLM relevance labels with ranking abilities . they take both LLM generated relevance labels and pairwise preferences . |
| Outcome: | The proposed method balances the ranking and labeling abilities of large language models . it takes both LLM generated relevance labels and pairwise preferences . |
LiPO: Listwise Preference Optimization through Learning-to-Rank (2025.naacl-long)
Copied to clipboard
Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J Liu, Xuanhui Wang
| Challenge: | Recent work on language models with curated feedback provides promising alternatives to RLHF . multiple responses can be ranked by reward models or AI feedback, but there is no study on directly fitting upon a list of responses. |
| Approach: | They propose a method that aligns language models with curated human feedback . they propose SLiC and DPO as promising alternatives to traditional RLHF . |
| Outcome: | The proposed method outperforms DPO and SLiC on several preference alignment tasks with curated and real rankwise preference data. |
Are Pretrained Convolutions Better than Pretrained Transformers? (2021.acl-long)
Copied to clipboard
| Challenge: | Recent research has shown promise in entirely convolutional, or CNN, architectures, but they have not been explored using the pre-train-fine-tune paradigm. |
| Approach: | They propose to use the pre-train-fine-tune paradigm to study convolutional models. |
| Outcome: | The proposed architectures outperform Transformers in certain scenarios, but with caveats. |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
Copied to clipboard
Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
Federated Data-Efficient Instruction Tuning for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing federated learning (FL) uses all local data, causing excessive computational overhead and overfitting to local data. |
| Approach: | They propose a federated data-efficient instruction tuning approach which utilizes a representative subset of edge-side data to tune LLMs. |
| Outcome: | The proposed method improves Rouge-L on unseen tasks by 10.72% over the SOTA full-data instruction tuning methods while using less than 1.5% of the data samples. |
The Devil in Linear Transformer (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing linear transformers suffer from performance degradations on various tasks and corpus. |
| Approach: | They propose a new linear attention that replaces scaling with a normalization to stabilize gradients and confine attention to neighbouring tokens in early layers. |
| Outcome: | The proposed model outperforms vanilla transformers on the long-range arena benchmark while being significantly more space-time efficient. |
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024.findings-naacl)
Copied to clipboard
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky
| Challenge: | Existing methods to rank documents using large language models do not understand these challenging ranking formulations. |
| Approach: | They propose to use Pairwise Ranking Prompting to improve ranking performance . they propose to outperform fine-tuned baseline rankers on benchmark datasets . |
| Outcome: | The proposed technique outperforms supervised baselines on benchmark datasets and outperformed other LLM-based solutions by over 10% on average. |
Accelerating Toeplitz Neural Network with Constant-time Inference Complexity (2023.emnlp-main)
Copied to clipboard
| Challenge: | Toeplitz Neural Networks outperform commonly used Transformer-based models while benefiting from log-linear space-time complexities. |
| Approach: | They propose to convert TNNs to SSMs during inference to combine strengths of TNN and SSM approaches. |
| Outcome: | The proposed method outperforms most Transformer-based models while retaining the advantage of constant inference complexity. |
Scaling Laws for Linear Complexity Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing scaling laws for large language models are unclear, but they are useful for scalability. |
| Approach: | They propose scaling laws for linear complexity language models to establish a foundation for their scalability. |
| Outcome: | The proposed models demonstrate superior linguistic proficiency and knowledge retention. |
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)
Copied to clipboard
Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions? |
| Approach: | They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods. |
| Outcome: | The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player. |
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |
PaRaDe: Passage Ranking using Demonstrations with LLMs (2023.findings-emnlp)
Copied to clipboard
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui
| Challenge: | Existing studies show that large language models can be instructed to perform zero-shot passage re-ranking . Existing work like UPR demonstrate promising results for zero- shot ranking using LLMs . |
| Approach: | They propose a demonstration selection strategy based on difficulty rather than semantic similarity . they propose to include only one demonstration in the prompt to improve re-ranking . |
| Outcome: | The proposed method improves LLM-based re-ranking by adding one demonstration to the prompt. |
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)
Copied to clipboard
Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
| Challenge: | Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs). |
| Approach: | They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models. |
| Outcome: | The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs. |
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
Copied to clipboard
Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
| Challenge: | State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. |
| Approach: | They propose to fine tune a pretrained encoder-decoder model using document to query generation. |
| Outcome: | The proposed model achieves comparable results to more expensive approaches while being 6.8X faster. |