Papers by Dongxu Li

7 papers
LAVIS: A One-stop Library for Language-Vision Intelligence (2023.acl-demo)

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Challenge: a new open-source library for language-vision research and applications is available for free.
Approach: They introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications.
Outcome: The proposed library is open-source and highly extensible and configurable.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)

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Challenge: Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries.
Approach: They propose a syntax tree network to address the complex text-to-SQL generation task.
Outcome: The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus.
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks (2025.findings-acl)

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Challenge: Solving expert-level multimodal tasks requires strong user query understanding, domain-specific knowledge, and advanced reasoning abilities.
Approach: They propose a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning.
Outcome: The proposed benchmark is publicly accessible at TBC.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)

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Challenge: Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models.
Approach: They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries .
Outcome: The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting.
The Devil in Linear Transformer (2022.emnlp-main)

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Challenge: Existing linear transformers suffer from performance degradations on various tasks and corpus.
Approach: They propose a new linear attention that replaces scaling with a normalization to stabilize gradients and confine attention to neighbouring tokens in early layers.
Outcome: The proposed model outperforms vanilla transformers on the long-range arena benchmark while being significantly more space-time efficient.
Automatic Gloss Dictionary for Sign Language Learners (2022.acl-demo)

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Challenge: 430 million people worldwide have developed hearing loss and 700 million more are learning a sign language as a second language . sign language learners have limited means of seeking assistance and are restricted to class offerings or relying on a webcam to look up the sign.
Approach: They propose an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs.
Outcome: The proposed system can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community.
Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)

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Challenge: Using a multimodal model, GUI agents can ground from language instructions to target elements . relying on HTML or AXTree inputs is a challenge for GUI agents .
Approach: They propose a large multimodal model specifically designed for GUI grounding that adopts a pure vision approach instead of auxiliary inputs.
Outcome: The proposed model outperforms vision-only and AXTree-reliant models on offline and online agents.

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