Papers by Frank Yang
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance (2025.emnlp-industry)
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Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, Bryan Hooi
| Challenge: | Existing large language model (LLM) agents are unable to adapt to changing domain knowledge and rules. |
| Approach: | They propose an LLM agent framework that continuously learns updated domain knowledge at test time. |
| Outcome: | The proposed agent improves on a customer due diligence name screening task on . the agent learns updated domain knowledge at test time. |
How is BERT surprised? Layerwise detection of linguistic anomalies (2021.acl-long)
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| Challenge: | a number of studies have shown that transformer-based language models detect when a word is anomalous in context, but likelihood scores do not tell the cause of the anomaly. |
| Approach: | They propose to use Gaussian models for density estimation at intermediate layers of three language models to evaluate grammaticality. |
| Outcome: | The proposed method on BLiMP shows that language models employ different mechanisms to detect different types of linguistic anomalies. |
LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection (2025.acl-industry)
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| Challenge: | e-commerce payment fraud detection is a new area for reinforcement learning (RL) and Large Language Models (LLMs). |
| Approach: | They propose to integrate reinforcement learning (RL) with Large Language Models (LLMs) by framing transaction risk as a multi-step Markov Decision Process (MDP), RL optimizes risk detection across multiple payment stages. |
| Outcome: | The proposed approach improves fraud detection accuracy and demonstrates zero-shot capability. |
An unsupervised framework for tracing textual sources of moral change (2021.findings-emnlp)
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| Challenge: | Existing studies on moral sentiment classification and temporal inference of moral sentiment have not quantified the origins of these changes. |
| Approach: | They propose an unsupervised framework for tracing textual sources of moral change toward entities through time. |
| Outcome: | The proposed framework captures fine-grained human moral judgments and identifies coherent source topics of moral change triggered by historical events. |
Neural reality of argument structure constructions (2022.acl-long)
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| Challenge: | lexicalist linguistic theories assume argument structure is predictable from meaning of verbs . construction grammarians propose argument structure constructions distinct from verbs. |
| Approach: | They adapt psycholinguistic studies to probe for the existence of argument structure constructions in Transformer-based language models. |
| Outcome: | The proposed method could be used to probe argument structure constructions in LMs . the study shows that LM learners prefer grouping by construction over verb grouping . |
Word class flexibility: A deep contextualized approach (2020.emnlp-main)
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| Challenge: | Existing studies on word class flexibility have been fraught with difficulties in quantifying it accurately and at scale. |
| Approach: | They propose a method to quantify word class flexibility in 37 languages using contextualized word embeddings. |
| Outcome: | The proposed method builds on recent work in contextualized word embeddings to quantify semantic shift between word classes and uncovers shared tendencies in class flexibility across languages. |
Active Retrieval Augmented Generation (2023.emnlp-main)
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Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
| Challenge: | Generative language models (LMs) have a tendency to hallucinate and create inaccurate output. |
| Approach: | They propose a method which iteratively uses a prediction of the upcoming sentence to anticipate future content. |
| Outcome: | The proposed method achieves superior or competitive performance on all tasks . iteratively uses a prediction of the upcoming sentence to anticipate future content . |