Papers by Andrew Zhao
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing work on cross-lingual word embeddings rely on linear mappings, but this assumption is not true for all language pairs. |
| Approach: | They propose a non-linear mapping approach which can find non-linesar relationships between languages by kernel Canonical Correlation Analysis. |
| Outcome: | The proposed approach improves on five language pairs on supervised and self-learning scenarios. |
Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)
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Yuhang Chen, Zhen Tan, Ajay Kumar Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen
| Challenge: | Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped. |
| Approach: | They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation . |
| Outcome: | The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
Mutual-Learning Improves End-to-End Speech Translation (2021.emnlp-main)
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| Challenge: | Existing approaches to end-to-end speech translation (E2E) models only allow one way knowledge transfer, which is limited by the performance of the teacher model. |
| Approach: | They propose a one-way knowledge transfer paradigm where the MT and ST models are collaboratively trained and considered as peers rather than teacher/student. |
| Outcome: | The proposed model improves the performance of end-to-end speech translation (ST) task by combining knowledge from two models with peer models. |
Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)
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Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | Existing bias mitigation techniques have a negative effect on NRE, a study finds . |
| Approach: | They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE . |
| Outcome: | The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set. |
Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)
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| Challenge: | Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost. |
| Approach: | They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters. |
| Outcome: | Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources. |
ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access (2026.eacl-demo)
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Jiwoo Park, Ruoqi Liu, Avani Jagdale, Andrew Srisuwananukorn, Jing Zhao, Lang Li, Ping Zhang, Sachin Kumar
| Challenge: | ClinicalTrialsHub consolidates clinical trial data from ClinicalTrial.gov and augments it by extracting and structuring trial-relevant information from PubMed. |
| Approach: | They propose a search-focused platform that consolidates PubMed data and extracts structured trial information. |
| Outcome: | ClinicalTrialsHub increases access to structured clinical trial data by 83.8% compared to ClinicalTrial.gov alone. |
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval (2024.emnlp-main)
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| Challenge: | Experimental results show that using only bi-encoders as an intermediate reranker can improve top-1 accuracy with negligible slowdown (7%). |
| Approach: | They propose a framework that compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
| Outcome: | The proposed framework compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition (D19-1)
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| Challenge: | Existing methods for humor recognition require a large amount of training data with labels to learn effective features. |
| Approach: | They propose a tensor embedding method that can extract lexical humor features for continuous humor recognition by using word-word co-occurrence to encode contextual content of documents, and then decompose the tenor to get corresponding vector representations. |
| Outcome: | The proposed method achieves a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B, but without the need for any external training corpus. |
Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
| Approach: | They propose a method that extracts a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers. |
| Outcome: | The proposed method outperforms baselines on NaturalQuestions and NewsQA by 11.5 and 8.4 EM. |
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation (2024.acl-long)
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Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum
| Challenge: | Low-resource tasks such as semi-supervised sequence generation require expert knowledge and cost. |
| Approach: | They propose a method for semi-supervised sequence generation where few examples are too scarce to fine tune a model. |
| Outcome: | The proposed method can generalize better than its teacher to unseen examples on semi-supervised sequence generation tasks. |
Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)
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Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang
| Challenge: | Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents. |
| Approach: | They propose a cognitive framework that incorporates first-order and second-order perspective transitions into LLMs to enhance their ability to identify and counteract deceptive information. |
| Outcome: | The proposed framework enhances LLMs’ ability to identify and counteract deceptive information without extra fine-tuning and data. |
Are My Optimized Prompts Compromised? Exploring Vulnerabilities of LLM-based Optimizers (2026.eacl-long)
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| Challenge: | Recent studies have focused on poisoning during supervised fine-tuning, RLHF, or inference-time time optimization. |
| Approach: | They propose a simple fake reward attack that requires no access to the reward model and significantly increases vulnerability. |
| Outcome: | The proposed attack reduces the fake reward ASR from 0.23 to 0.07 without degrading utility. |