Papers by Yanguang Liu
FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise Prediction (2026.acl-long)
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| Challenge: | Existing models for earnings surprise prediction rely on expensive, proprietary data. |
| Approach: | They propose to use textual transcripts and audio recordings to build a dataset for earnings surprise prediction. |
| Outcome: | The proposed dataset includes 2,688 unique conference calls from 2019 to 2021. |
SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection (2026.findings-acl)
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| Challenge: | Conference call transcripts contain significant redundancy and industry-specific terminology that creates obstacles for language models. |
| Approach: | They propose a Sparse Autoencoder for Financial Representation Enhancement framework to extract key information from earnings conference call transcripts and eliminate redundancy. |
| Outcome: | The proposed method outperforms baselines in analyzing earnings conference call transcripts. |
FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models (2026.acl-long)
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| Challenge: | FinChart-Bench is the first benchmark specifically focused on real-world financial charts. |
| Approach: | They propose a benchmark specifically focused on real-world financial charts. |
| Outcome: | The proposed benchmark evaluates 26 state-of-the-art LVLMs on FinChart-Bench. |