Papers by Yuanjian Xu
BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on narrow tasks and leave a fundamental question unanswered . Existing models only focus on specific tasks, requiring rigorous reasoning and knowledge . |
| Approach: | They propose a benchmark to connect theoretical foundations with practical business knowledge and applications. |
| Outcome: | The benchmark systematically evaluates both open-source and commercial LLMs . it reveals how theoretical knowledge translates into practical performance in business . |
Hard Sample Aware Prompt-Tuning (2023.acl-long)
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| Challenge: | Prompt-tuning based few-shot learning has garnered increasing attention in recent years due to its efficiency and promising capability. |
| Approach: | They propose a framework to distinguish informative hard samples from misleading ones in model training. |
| Outcome: | The proposed framework achieves new SOTA results on a series of NLP tasks pushing the SST-5 accuracy to 49.5% (1.1% point absolute improvement), QNLI accuracy to 74.6% (1.9% absolute improvement) |
Rethinking Data Mixing from the Perspective of Large Language Models (2026.acl-short)
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Yuanjian Xu, Tianze Sun, Changwei Xu, XinLong Zhao, Jianing Hao, Ran Chen, Yang Liu, Ruijie Xu, Stephen Chen, Guang Zhang
| Challenge: | Existing methods to mix data with LLMs have relied on domain definitions derived from intuition. |
| Approach: | They propose a reweighting framework that restructures data scheduling as a graph-constrained optimization problem. |
| Outcome: | The proposed framework achieves competitive performance on GPT-2 models. |
FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness (2025.findings-acl)
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| Challenge: | Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities. |
| Approach: | They propose a framework that empowers large language models to analyze ripple effects . they use financial theory-guided large-scale reinforcement learning to align LLMs with the market . |
| Outcome: | The proposed framework allows LLMs to analyze ripple effects through financial theory-guided large-scale reinforcement learning. |