Papers by Ryan Li
A Measure-Theoretic Characterization of Tight Language Models (2023.acl-long)
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| Challenge: | Language modeling is a core task in natural language processing. |
| Approach: | They propose to characterize leakage onto the set of infinite sequences by a measure-theoretic approach. |
| Outcome: | The proposed language model families are tight, meaning they will not leak . the proposed language models are based on the 'sequence leakage' hypothesis . |
Hallucination Diversity-Aware Active Learning for Text Summarization (2024.naacl-long)
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| Challenge: | Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness . |
| Approach: | They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling. |
| Outcome: | The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs. |
A Formal Perspective on Byte-Pair Encoding (2023.findings-acl)
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| Challenge: | Byte-Pair Encoding (BPE) is a popular algorithm used for tokenizing data in NLP, but the underlying optimization problem that BPE seeks to solve has not yet been laid down. |
| Approach: | They propose an algorithm which is a 1/sigma*(1-e(-sigma))-approximation of an optimal merge sequence. |
| Outcome: | The proposed algorithm improves the runtime complexity from O(NM) to O(N log M) and the lower bound of the approximation is approx0.37. |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
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Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Edge: Enriching Knowledge Graph Embeddings with External Text (2021.naacl-main)
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| Challenge: | Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. |
| Approach: | They propose a knowledge graph enrichment framework called Edge to enhance knowledge graphs based on "hard" co-occurrence of words in knowledge graph entities and external text. |
| Outcome: | The proposed framework achieves "soft" augmentation by combining external text with knowledge graph entities. |
When is a Language Process a Language Model? (2024.findings-acl)
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| Challenge: | In some pathological situations, such a stochastic process may "leak" probability mass onto the set of infinite strings. |
| Approach: | They propose to view a language model as a discrete stochastic process X t : t = = t + . |
| Outcome: | The proposed conditions of tightness are generalized to language models and the literature. |
Unique Hard Attention: A Tale of Two Sides (2025.acl-short)
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| Challenge: | Understanding the expressive power of transformers has attracted attention . many studies analyze unique hard attention transformers, where attention selects a single position that maximizes the attention scores. |
| Approach: | They propose to use unique hard attention to select a single position that maximizes attention scores . they show that models with leftmost-hard attention are equivalent to soft attention . |
| Outcome: | The proposed models with leftmost-hard attention are equivalent to soft attention, suggesting they may better approximate real-world transformers than right-attention models. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression (2026.findings-eacl)
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| Challenge: | Low-rank decomposition methods suffer from accuracy degradation and expensive calibration procedures. |
| Approach: | They propose a fast and accurate, training-free structural compression method based on fine-grained low-rank transformations in the activation space. |
| Outcome: | The proposed method outperforms pruning baselines in generalization and downstream performance while delivering inference speedups. |
A Transformer with Stack Attention (2024.findings-naacl)
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| Challenge: | Recent research suggests that transformer-based language models fail to learn basic algorithmic patterns. |
| Approach: | They propose to augment transformer-based language models with a differentiable stack-based attention mechanism that adds a level of interpretability to the model. |
| Outcome: | The proposed model can model some, but not all, deterministic context-freelanguages. |
Characterizing the Expressivity of Local Attention in Transformers (2026.acl-long)
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| Challenge: | Existing studies show that global and local attention are expressively complementary. |
| Approach: | They propose to restrict global attention to a fixed-size window of preceding tokens . they also propose to add local attention to local-only transformers to increase model quality . |
| Outcome: | The proposed model outperforms the global–local transformers on formal language recognition and natural language modeling. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students’ Hand-Drawn Math Images (2025.naacl-long)
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| Challenge: | DrawEduMath examines the ability of vision language models to handle real-world math problems, such as those encountered in classrooms and tutoring sessions. |
| Approach: | They present DrawEduMath, an English-language dataset of 2,030 images of students’ handwritten responses to math problems. |
| Outcome: | The proposed model can be used to evaluate teachers' QA pairs and 44,362 synthetic QAs derived from teachers' descriptions. |
Mind the Gap: Static and Interactive Evaluations of Large Audio Models (2025.acl-long)
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Minzhi Li, William Barr Held, Michael J Ryan, Kunat Pipatanakul, Potsawee Manakul, Hao Zhu, Diyi Yang
| Challenge: | Recent work has focused on evaluating large audio models (LAMs) that directly accept audio inputs. |
| Approach: | They propose an interactive approach to evaluate large audio models and collect 7,500 LAM interactions from 484 participants. |
| Outcome: | The proposed model is based on a set of user-generated audio interfaces with 7,500 interactions from 484 participants. |
On the Representational Capacity of Recurrent Neural Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have focused on LMs as formal languages, but they do not consider language membership. |
| Approach: | They extend the Turing completeness result to the probabilistic case . they show that a rationally weighted RLM can simulate any deterministic Turing machine . |
| Outcome: | The proposed model can simulate any deterministic Turing machine with rationally weighted transitions . the proposed model is based on recurrent neural networks with a rational weighting over strings . |
Differentiable Subset Pruning of Transformer Heads (2021.tacl-1)
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| Challenge: | Recent work shows that a large proportion of the heads in a Transformer’s multi-head attention mechanism can be safely pruned away without significantly harming the performance of the model. |
| Approach: | They propose a method that prunes a Transformer's multi-head attention mechanism away without significantly harming its performance. |
| Outcome: | The proposed method improves on natural language inference and machine translation tasks while offering precise control of sparsity level. |
Distilling an End-to-End Voice Assistant Without Instruction Training Data (2025.acl-long)
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| Challenge: | Recent efforts to train speech-only LLMs have led to models “forging” speech information from text-only models. |
| Approach: | They propose a paradigm for training Speech Large Language Models without instruction data by using the response of a text-only LLM to transcripts as self-supervision. |
| Outcome: | The proposed model generalizes to Spoken Question Answering, Classification, and Translation and achieves a 72% win rate compared with state-of-the-art models like Qwen 2 Audio . |
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World (2025.findings-acl)
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Sina Semnani, Pingyue Zhang, Wanyue Zhai, Haozhuo Li, Ryan Beauchamp, Trey Billing, Katayoun Kishi, Manling Li, Monica Lam
| Challenge: | Using a partially reannotated subset of the Armed Conflict Location & Event Data, we analyze 39,786 conflict events across 20 languages and 171 countries. |
| Approach: | They propose a large-scale conflict event dataset with extensive coverage of region-specific entities. |
| Outcome: | The proposed method detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. |
Tokenization and the Noiseless Channel (2023.acl-long)
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| Challenge: | Subword tokenization is a key part of most NLP pipelines, but little is known about why some combinations lead to improved downstream model performance. |
| Approach: | They propose that good tokenizers lead to efficient channel usage . they propose that an optimal encoding assigns extremely long codes to low-frequency subwords . |
| Outcome: | The proposed tokenizers have a very strong correlation with BLEU in machine translation . the proposed function can be used to improve model performance in the downstream task . |
Common Law Annotations: Investigating the Stability of Dialog System Output Annotations (2023.findings-acl)
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Seunggun Lee, Alexandra DeLucia, Nikita Nangia, Praneeth Ganedi, Ryan Guan, Rubing Li, Britney Ngaw, Aditya Singhal, Shalaka Vaidya, Zijun Yuan, Lining Zhang, João Sedoc
| Challenge: | High agreement is often used to show reliability of annotation procedures, but it is insufficient to ensure or reproducibility. |
| Approach: | They propose a protocol that increases Inter-Annotator Agreement among annotators and a standardized and codified protocol that strictly enforces transparency in the annotation process. |
| Outcome: | The proposed protocol ensures transparency in the annotation process, which ensures reproducibility of annotation guidelines. |
What Do Language Models Learn in Context? The Structured Task Hypothesis. (2024.acl-long)
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| Challenge: | Pre-trained large language models have exhibited an impressive ability to learn in context across various domains, e.g., code generation, education, medicine and even medicine. |
| Approach: | They taxonomize existing candidate theories into three competing hypotheses that explain LLMs’ ability to learn in context. |
| Outcome: | The proposed model can learn a task from in-context examples presented in a demonstration and generalize it to the prompt. |
GUI Agents: A Survey (2025.findings-acl)
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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies (2024.findings-emnlp)
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| Challenge: | CultureBank is a knowledge base built upon users’ self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. |
| Approach: | They construct a pipeline to construct cultural knowledge bases from different online communities on a massive scale. |
| Outcome: | The proposed pipeline improves cultural awareness of language models by evaluating them on two cultural tasks in a zero-shot setting. |
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)
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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya
| Challenge: | Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA . |
| Approach: | They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities. |
| Outcome: | a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region . |
Probing via Prompting (2022.naacl-main)
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| Challenge: | Pre-trained language models have increased the performance of data-driven natural language processing (NLP) models on a wide variety of tasks. |
| Approach: | They propose a model-free approach to probing via prompting which formulates probing as a prompting task and combine pruning to analyze where the model stores the linguistic information in its architecture. |
| Outcome: | The proposed approach extracts information from pre-trained models while learning much less on its own. |
Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering (2025.naacl-long)
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| Challenge: | Generative AI has made rapid advances in multimodal understanding and code generation. |
| Approach: | They construct a first real-world benchmark for multimodal large language models that directly convert visual designs into code implementations by manually curating 484 diverse real-life webpages as test cases. |
| Outcome: | The proposed model can generate code implementations that directly render into the given reference webpages, given the screenshots as input. |
Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping (2025.naacl-long)
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| Challenge: | Existing research on UI/UX automation often requires high-fidelity inputs like Figma designs or detailed screenshots, limiting accessibility and impeding efficient design iteration. |
| Approach: | They propose a benchmark that evaluates state-of-the-art Vision Language Models on converting sketches into webpage prototypes. |
| Outcome: | The benchmark evaluates state-of-the-art Vision Language Models on automating the conversion of rudimentary sketches into webpage prototypes. |