Papers by Joey Tianyi Zhou
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is an important step in most natural language processing (NLP) applications. |
| Approach: | They propose a dual-adversarial neural transfer method for addressing low-resource Named Entity Recognition (NER) they propose 'Generalized Resource-Adversarial Discriminator' and 'accidental training' |
| Outcome: | The proposed method improves on low-resource Named Entity Recognition (NER) with two variants, i.e., DATNet-F and DATNET-P, and adversarial training is adopted to boost model generalization. |
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering (2020.acl-main)
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| Challenge: | Existing MCQA datasets are small in size, which increases difficulty of model learning and generalization. |
| Approach: | They propose a multi-source meta transfer framework for low-resource multiple-choice question answering . they extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains . |
| Outcome: | The proposed framework is independent of backbone language models and can bridge the distribution gap between training sources and target. |
Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)
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| Challenge: | Existing approaches to NLVL are either ranking tasks or regressing the target video span. |
| Approach: | They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach. |
| Outcome: | The proposed network outperforms the state-of-the-art methods on three benchmark datasets. |
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)
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| Challenge: | Current summarization systems only produce plain, factual headlines, far from the practical needs for exposure and memorableness of the articles. |
| Approach: | They propose a task to generate relevant headlines with three style options . they propose combining summarization and reconstruction tasks into a multitasking framework . |
| Outcome: | The proposed method outperforms the state-of-the-art summarization model by 9.68% . it can generate relevant, fluent headlines with humor, romance and clickbait . |
Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents (2025.findings-emnlp)
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| Challenge: | Existing approaches to large language models are limited to historical backtesting and static data. |
| Approach: | a new large-language model is developed to simulate real-time trading in a virtual stock market . the agent trading arena simulates real-world bid-ask interactions and provides real-life trading scenarios . |
| Outcome: | The Agent Trading Arena simulates real-world market conditions and directly impacts price dynamics. |
Parallel Attention Network with Sequence Matching for Video Grounding (2021.findings-acl)
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| Challenge: | Existing approaches to video grounding are sensitive to quality of proposals and inefficient because all proposal-query pairs are compared. |
| Approach: | They propose a Parallel Attention Network with Sequence matching to capture selfmodal contexts and cross-modal attentive information between video and text. |
| Outcome: | The proposed approach is superior to state-of-the-art methods on three datasets. |
Understanding Large Language Model Vulnerabilities to Social Bias Attacks (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic capabilities across tasks . however, there is a growing concern about their potential to perpetuate social biases . |
| Approach: | They evaluate LLMs across gender, racial, and religious bias types . they also explore cross-bias and multiple-biases attacks . |
| Outcome: | The proposed models are more susceptible to gender bias attacks than racial or religious biases. |
Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)
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| Challenge: | Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. |
| Approach: | They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase. |
| Outcome: | The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction. |
SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)
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| Challenge: | Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning. |
| Approach: | They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation. |
| Outcome: | The proposed framework significantly improves accuracy over baselines on large-scale benchmarks. |
DiffPO: Diffusion-styled Preference Optimization for Inference Time Alignment of Large Language Models (2025.acl-long)
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Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu
| Challenge: | Inference-time alignment approaches still face limitations due to policy-specific value functions and latency during the inference phase. |
| Approach: | They propose an efficient and policy-agnostic preference optimization method that avoids time latency associated with token generation. |
| Outcome: | The proposed method achieves a favorable trade-off between alignment quality and inference-time latency. |
CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching (2025.emnlp-main)
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| Challenge: | Existing methods for dataset condensation struggle to compress more information into samples . current methods struggle to extract enough training data for effective learning . |
| Approach: | They propose a paradigm for dataset condensation that uses an LLMs-driven approach to generate more informative and less biased samples. |
| Outcome: | The proposed method outperforms coreset selection and existing condensation methods by large margins while significantly reducing the computational cost. |