Papers by Lin Hai
Omni-Chart-600K: A Comprehensive Dataset of Chart Types for Chart Understanding (2025.findings-naacl)
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Shulei Wang, Shuai Yang, Wang Lin, Zirun Guo, Sihang Cai, Hai Huang, Ye Wang, Jingyuan Chen, Tao Jin
| Challenge: | Existing chart-related training methods lack capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types. |
| Approach: | They propose a two-stage training strategy and method for jointly training a vision encoder tailored for multi-type charts to address the deficiencies in chart types and limited scope of chart tasks in existing datasets. |
| Outcome: | The proposed dataset includes 21 diverse chart types and tasks, including data retrieval and mathematical reasoning. |
GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)
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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim
| Challenge: | Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy. |
| Approach: | They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. |
| Outcome: | Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency. |
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have made great progress in text generation but suffer from hallucinations during reasoning and generation. |
| Approach: | They propose an inference-time method to help LLMs decode truthfully by selecting tokens with the lowest probabilities and concatenating them to the original context. |
| Outcome: | The proposed method improves LLaMA-7b, LLama2-7b and Mistral-7b on hallucination tasks. |
OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models (2024.findings-naacl)
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| Challenge: | Existing methods for object navigation are limited to household datasets with close-set objects, and they lack the ability to generalize to new environments in a zero-shot manner. |
| Approach: | They propose a framework that leverages reasoning abilities of large vision language models to extract proposed objects from natural language instructions that meet the user’s demand. |
| Outcome: | The proposed framework surpasses baselines on all metrics and can be used in a HM3D ObjectNav benchmark. |
Enhancing Metaphor Detection by Gloss-based Interpretations (2021.findings-acl)
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| Challenge: | Existing approaches to metaphor detection are limited by ambiguous meanings of metaphorical substitute words. |
| Approach: | They propose a model that utilizes glosses to interpret metaphorical words by enhancing three datasets with gloss annotations. |
| Outcome: | The proposed model outperforms state-of-the-art models on three enhanced datasets and that gloss-based interpretation benefits metaphor detection. |