Papers by Andrew Zhang
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)
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Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
| Challenge: | Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages. |
| Approach: | They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets. |
| Outcome: | The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. |
SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models (2025.findings-emnlp)
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| Challenge: | SteerVLM is a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions. |
| Approach: | They propose a lightweight steering module that learns from latent embeddings of paired prompts encoding target and converse behaviors to dynamically adjust activations connecting the language modality with image context. |
| Outcome: | The proposed steering module outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for VLMs. |
Federated Learning of Gboard Language Models with Differential Privacy (2023.acl-industry)
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Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, Yuanbo Zhang
| Challenge: | Using federated learning and differential privacy, we train and deploy language models with federation and DP in Google Keyboard. |
| Approach: | They train and deploy language models with federated learning and differential privacy in Google Keyboard . |
| Outcome: | The proposed algorithm achieves meaningfully formal DP guarantees without uniform sampling of clients. |
Unifying Inference-Time Planning Language Generation (2026.findings-acl)
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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang
| Challenge: | Large language models (LLMs) are used to generate a formal representation of a plan in a planning language. |
| Approach: | They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning. |
| Outcome: | The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages. |
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)
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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger
| Challenge: | Language identification (LID) is a fundamental step in curating multilingual corpora. |
| Approach: | They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. |
| Outcome: | The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain. |
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes (2022.lrec-1)
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| Challenge: | Biomedical researchers have used manual curation to extract biomedical interactions from research texts to improve coverage. |
| Approach: | They propose a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models using human annotations and the CTD database. |
| Outcome: | The proposed dataset is substantially larger and cleaner than existing datasets and includes annotations linking mentions to their entities. |
Learning Dialogue Representations from Consecutive Utterances (2022.naacl-main)
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| Challenge: | Dialogue Sentence Embedding (DSE) is a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue-oriented tasks. |
| Approach: | They propose a self-supervised contrastive learning method that learns dialogue representations suitable for a wide range of dialogue tasks. |
| Outcome: | The proposed method outperforms baselines on five dialogue tasks on a few-shot and zero-shot datasets. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
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| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
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| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
| Approach: | They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB) |
| Outcome: | The proposed method improves state-of-the-art for OpenIE extractions and boosts performance on OpenIE from semi-structured data. |
Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Large Vision-Language Models have demonstrated impressive performance on vision-language reasoning tasks, but their potential for zero-shot fine-grained image classification remains underexplored. |
| Approach: | They propose a method that transforms zero-shot fine-grained image classification into a visual question-answering framework. |
| Outcome: | The proposed method outperforms the current state-of-the-art approach and outperformed existing methods. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval (2025.acl-long)
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| Challenge: | Existing approaches to cross-modal image-text retrieval struggle with nuanced cross-modal relationships. |
| Approach: | They propose a set-based approach that represents each sample with multiple embeddings to capture nuanced and diverse relationships. |
| Outcome: | The proposed method achieves state-of-the-art performance on MS-COCO and Flickr30k without external data. |
AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
| Challenge: | AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs . |
| Approach: | They propose a document-level multi-parallel translation dataset covering English and five African languages. |
| Outcome: | The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models . |
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. |
Enhanced Distant Supervision with State-Change Information for Relation Extraction (2022.lrec-1)
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| Challenge: | Existing methods for enhancing distant supervision with state-change information for relation extraction are limited. |
| Approach: | They propose a method for enhancing distant supervision with state-change information for relation extraction by adding temporal information to a curation dataset. |
| Outcome: | The proposed method reduces noise when used for static relation extraction and can be used to train a relation-extraction system that detects a change of state in relations. |
Event-Event Relation Extraction using Probabilistic Box Embedding (2022.acl-short)
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| Challenge: | Existing frameworks of event relation extraction do not guarantee coherence between different relation types, such as anti-symmetry. |
| Approach: | They propose to modify existing ERE framework to guarantee coherence by representing each event as a box representation without applying explicit constraints. |
| Outcome: | The proposed model shows stronger conjunctive constraint satisfaction compared to previous models with constraint injection. |
Flexible-length Text Infilling for Discrete Diffusion Models (2025.emnlp-main)
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| Challenge: | Existing discrete diffusion models lack flexibility for text infilling without ground-truth positional data. |
| Approach: | They propose a discrete diffusion model that jointly denoises token values and token positions using a novel sample-level Optimal Transport coupling. |
| Outcome: | The proposed method outperforms existing methods on infilling benchmarks such as One-Billion-Word and Yelp. |
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning (2025.acl-long)
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Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang, Chuyu Liu, Benjamin Van Durme, Daniel Khashabi
| Challenge: | RATIONALYST is a model for process-supervision of reasoning based on pretraining on rationale annotations extracted from unlabeled data. |
| Approach: | They propose a model for process-supervision of reasoning based on pre-training on rationale annotations extracted from unlabeled data. |
| Outcome: | RATIONALYST improves reasoning accuracy by 3.9% on representative reasoning benchmarks. |
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)
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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin
| Challenge: | Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors . |
| Approach: | They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents. |
| Outcome: | The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries. |
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2022.naacl-main)
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| Challenge: | Pretrained language models are typically learned over a large, static corpus and fine-tuned for various downstream tasks. |
| Approach: | They propose to continuously update a pretrained language model to adapt to emerging data and to keep track of the model's performance. |
| Outcome: | The proposed model can adapt to new corpora while retaining knowledge in earlier domains. |
Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies (2023.findings-acl)
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| Challenge: | Using a method that uses text embeddings, we find that human judges prefer manuscript matches from our method in 70% of cases. |
| Approach: | They propose to use text embeddings to integrate a method to estimate the causal effects of peer review policies in publication venues that shift policies from single-blind to double-blinded. |
| Outcome: | The proposed method is compared with a widely-used method of stratified propensity score matching and a baseline of randomly selected matches. |
Automated Few-Shot Classification with Instruction-Finetuned Language Models (2023.findings-emnlp)
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| 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. |
ReasonerRank: Redefining Language Model Evaluation with Ground-Truth-Free Ranking Frameworks (2025.findings-acl)
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Jiamu Zhang, Jiayi Yuan, Andrew Wen, Hoang Anh Duy Le, Yu-Neng Chuang, Soo-Hyun Choi, Rui Chen, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly adopted across real-world applications . traditional evaluations rely on expensive, domain-specific ground-truth labels . obtaining labeled data is expensive, time-consuming, and often requires domain expertise . |
| Approach: | They propose a ground-truth-free evaluation framework focused on reasoning consistency and instruction following. |
| Outcome: | The proposed framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches. |
Tree-of-Quote Prompting Improves Factuality and Attribution in Multi-Hop and Medical Reasoning (2025.emnlp-main)
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Justin Xu, Yiming Li, Zizheng Zhang, Augustine Yui Hei Luk, Mayank Jobanputra, Samarth Oza, Ashley Murray, Meghana Reddy Kasula, Andrew Parker, David W Eyre
| Challenge: | Large language models (LLMs) produce fluent but factually incorrect outputs, a phenomenon commonly referred to as hallucination. |
| Approach: | They propose a Tree-of-Quote framework that decomposes complex questions into subquestions and generates quotes to support each step without retrieval. |
| Outcome: | Experiments on StrategyQA, 2WikiMultiHopQA, MuSiQue, MoreHopQ, and MedQA show that ToQ improves factuality and attribution over baselines. |
Deduplicating Training Data Makes Language Models Better (2022.acl-long)
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Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini
| Challenge: | Existing language modeling datasets contain near-duplicate examples and long repetitive substrings. |
| Approach: | They develop tools that allow us to deduplicate existing language modeling datasets . they found that over 1% of the unprompted output of language models is copied verbatim . |
| Outcome: | The proposed tools reduce train-test overlap, which affects over 4% of validation sets, and improve model accuracy. |
Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)
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| Challenge: | Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. |
| Approach: | They propose a virtual augmentation supported Contrastive Learning of sentence representations . they approximate the neighborhood of an instance via its K-nearest in-batch neighbors . |
| Outcome: | The proposed model outperforms existing methods on a wide range of downstream tasks. |