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Jindong Chen

14 accepted papers

2026

Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos

CVPR 2026

Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limitation: they focus entirely on web-based interaction and perception, lacking grounding in the user's real-world physical s

Cited by 0SourcecodeScholar
2025

Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection

ICLR 2025poster

Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linearly in the number of retrieved documents, causing a dramatic increase in latency. In this paper, we propose a novel para…

Cited by 16SourcePDFScholar
2025

ScreenQA: Large-Scale Question-Answer Pairs Over Mobile App Screenshots

NAACL 2025long

We introduce ScreenQA, a novel benchmarking dataset designed to advance screen content understanding through question answering. The existing screen datasets are focused either on low-level structural and component understanding, or on a much higher-level composite task such as navigation and task c…

2024

Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs

NAACL 2024findings

Vision-language models (VLMs) are achieving increasingly strong performance on multimodal tasks. However, reasoning capabilities remain limited particularly for smaller VLMs, while those of large-language models (LLMs) have seen numerous improvements. We pro-pose a technique to transfer capabilities…

2024

Fusion-Eval: Integrating Assistant Evaluators with LLMs

EMNLP 2024industry

Evaluating natural language generation (NLG) systems automatically poses significant challenges.Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. However, these methods still show lower correspondence wi…

2024

RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

NAACL 2024system demonstrations

The recent progress of AI can be largely attributed to large language models (LLMs). However, their escalating memory requirements introduce challenges for machine learning (ML) researchers and engineers. Addressing this requires developers to partition a large model to distribute it across multiple…

2024

RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting

AAAI 2024technical

Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final text results rather than intermediate revisions, it might be challenging for them to perform text rewriting tasks. Mos…

2024

ScreenAI: A Vision-Language Model for UI and Infographics Understanding

IJCAI 2024poster

Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves…

2024

Towards an On-device Agent for Text Rewriting

NAACL 2024findings

Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting. However creating a smaller yet potent language model for text rewriting presents two formidable challenges: costly data collection and absence of emergent capabilities.In this paper we present solutions to add…

Cited by 11SourcePDFScholar
2023

Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer

NeurIPS 2023poster

Large language models (LLMs) such as T0, FLAN, and OPT-IML excel in multi-tasking under a unified instruction-following paradigm, where they also exhibit remarkable generalization abilities to unseen tasks. Despite their impressive performance, these LLMs, with sizes ranging from several billion to…

Cited by 9SourcePDFScholar
2022

Towards Better Semantic Understanding of Mobile Interfaces

COLING 2022main

Improving the accessibility and automation capabilities of mobile devices can have a significant positive impact on the daily lives of countless users. To stimulate research in this direction, we release a human-annotated dataset with approximately 500k unique annotations aimed at increasing the und…

2021

ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces

AAAI 2021technical

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibility of these devices and to enable their usage in a variety of settings, building models that can assist users and accom…

Cited by 82SourcePDFScholar
2021

PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling

ACL 2021long

We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two t…

2021

UIBert: Learning Generic Multimodal Representations for UI Understanding

IJCAI 2021poster

To improve the accessibility of smart devices and to simplify their usage, building models which understand user interfaces (UIs) and assist users to complete their tasks is critical. However, unique challenges are proposed by UI-specific characteristics, such as how to effectively leverage multimod…