Papers by Brian Wang
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit surprising abilities across a variety of language tasks. |
| Approach: | They propose an algorithm which selects a coreset by analyzing correlation between training and evaluation samples with a trained model. |
| Outcome: | The proposed algorithm can achieve similar performance with just 50% of the training data while preserving the accuracy of the existing model. |
Intersectional Stereotypes in Large Language Models: Dataset and Analysis (2023.findings-emnlp)
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| Challenge: | Existing studies on intersectional stereotypes focus on broader, individual categories . current studies focus on single-group stereotypes, such as racial bias against African Americans . |
| Approach: | They propose to use a dataset of intersectional stereotypes curated with the ChatGPT model to analyze propagation in three contemporary LLMs. |
| Outcome: | The proposed dataset enables analysis of stereotype propagation in three contemporary LLMs. |
Efficient Large Scale Language Modeling with Mixtures of Experts (2022.emnlp-main)
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Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giridharan Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeffrey Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Veselin Stoyanov
| Challenge: | Mixture of Experts layers (MoEs) enable efficient scaling of language models . large autoregressive language models such as GPT-3 can be adapted to a wide range of tasks . |
| Approach: | They propose to use Mixture of Experts layers to enable efficient scaling of language models . they find that MoEs are substantially more compute efficient than dense models compared to MoE models - but only when they are more modestly trained . |
| Outcome: | The proposed model outperforms dense models in a wide range of tasks and domains. |
Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)
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Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li
| Challenge: | Large-scale generative language models such as GPT-3 are competitive few-shot learners. |
| Approach: | They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities. |
| Outcome: | The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions. |
Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise (2025.emnlp-main)
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Hanyin Wang, Chufan Gao, Qiping Xu, Bolun Liu, Guleid Hussein, Hariprasad Reddy Korsapati, Mohamad El Labban, Kingsley Iheasirim, Mohamed Hassan, Gokhan Anil, Brian Bartlett, Jimeng Sun
| Challenge: | Currently, no automated, scalable method exists to evaluate the quality of LLM-generated clinical notes, leaving manual evaluation the gold standard. |
| Approach: | They propose a framework for training PRMs to deliver step-level reward signals for LLM-generated clinical notes. |
| Outcome: | The proposed framework outperforms reasoning and non-reasoning models on key evaluations and selects physician-preferred clinical notes with 56.2% accuracy. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
Deciphering Stereotypes in Pre-Trained Language Models (2023.emnlp-main)
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Weicheng Ma, Henry Scheible, Brian Wang, Goutham Veeramachaneni, Pratim Chowdhary, Alan Sun, Andrew Koulogeorge, Lili Wang, Diyi Yang, Soroush Vosoughi
| Challenge: | Current approaches for examining stereotypes in PLMs require intricate human knowledge about these stereotypes and entail careful manual curation of examples. |
| Approach: | They propose a framework for examining stereotype-encoding behavior of PLMs using model probing and textual analyses. |
| Outcome: | The proposed approach can debiase PLMs without compromising their language modeling capabilities or performance. |
PhyVer: Physics-Grounded Material Claim Verification with Multi-Fidelity Physical Evidence (2026.acl-demo)
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| Challenge: | Existing claim verification pipelines operate over text, producing ungrounded judgments. |
| Approach: | They propose a physics-grounded material claim verification system that can be used to verify claims with physical evidence. |
| Outcome: | The proposed system reduces MAE and sign-offs with experts over text-only GPT-5.1. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)
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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang
| Challenge: | Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations. |
| Approach: | They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation. |
| Outcome: | The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement. |
Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains (2024.acl-short)
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| Challenge: | a new dataset examines whether fine-tuned metrics are robust to domain shifts between training and inference. |
| Approach: | They use an annotated multidimensional quality metrics dataset to examine whether they are robust to domain shifts between training and inference. |
| Outcome: | The proposed metrics exhibit a substantial performance drop in the unseen domain scenario compared to metrics that rely on the surface form and pre-trained metrics that are not fine-tuned on MT quality judgments. |
End-to-End Optimization for Multimodal Retrieval-Augmented Generation via Reward Backpropagation (2025.findings-emnlp)
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| Challenge: | MM-RAG is a promising approach for enhancing the reliability and factuality of large vision-language models . current methods focus on component-level optimizations and necessitate extensive component-specific training datasets . |
| Approach: | They propose a new paradigm that backpropagates global rewards to each component . this backpropage transforms local losses into specific local losses . |
| Outcome: | The proposed paradigm achieves high training efficiency on knowledge-intensive multimodal benchmarks. |
Inventive Problem Solving with LLMs: A Benchmark for TRIZ Reasoning (2026.findings-acl)
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| Challenge: | Large language models are increasingly used in inventive problem-solving but effective support requires more than open-ended idea generation. |
| Approach: | They propose a dataset and benchmark for TRIZ reasoning grounded in open technical sources and U.S. patents. |
| Outcome: | The proposed framework represents trade-offs and links them to standardized inventive principles. |
RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models (2024.findings-acl)
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| Challenge: | Traditionally, developing new language models involves fine-tuning pre-trained LMs . model merging is a cost-effective alternative to fine-timing LM models for multiple tasks . |
| Approach: | They propose an algorithm for merging fine-tuned language models without additional training. |
| Outcome: | The proposed algorithm outperforms baseline methods on multiple benchmarks. |
Improving Syntactic Probing Correctness and Robustness with Control Tasks (2023.acl-short)
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Weicheng Ma, Brian Wang, Hefan Zhang, Lili Wang, Rolando Coto-Solano, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Syntactic probing methods are biased by the PLMs’ memorization of common word co-occurrences, even if they do not form syntactical relations. |
| Approach: | They propose to use random word substitution and random label matching to reduce these biases and improve the robustness of syntactic probing methods. |
| Outcome: | The proposed tasks improve probing results and consistency between probing methods and make them more generalizable to unseen text domains. |