Papers by Fengyuan Hu
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning (2023.emnlp-main)
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| Challenge: | Pre-trained language models have shown impressive performance in various language tasks, but are prone to spurious correlations and illusory information. |
| Approach: | They propose to use pre-trained language models to justify decisions with formalized, coherent reasoning chains. |
| Outcome: | The proposed strategies improve coherence of rationalizations yielding state-of-the-art results on Tiered Reasoning for Intuitive Physics (TRIP). |
Eliciting In-Context Learning in Vision-Language Models for Videos Through Curated Data Distributional Properties (2024.emnlp-main)
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| Challenge: | Emergent In-context Learning on Videos induces in-contact learning over video and text . eILeV-trained models outperform other off-the-shelf VLMs in few-shot video narration for novel, rare actions. |
| Approach: | They implement Emergent In-context Learning on Videos (EILeV) that induces in-contact learning over video and text by capturing key properties of pre-training data. |
| Outcome: | The proposed training paradigm outperforms off-the-shelf VLMs in few-shot video narration for novel, rare actions. |
Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)
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| Challenge: | Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio. |
| Approach: | They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
| Outcome: | The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026.findings-acl)
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Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng, Qi Liu
| Challenge: | Existing benchmarks rely on outcome-driven metrics such as profitability and look-ahead bias. |
| Approach: | They propose a diagnostic benchmark for instruction-grounded financial code generation under strict semantic and temporal constraints. |
| Outcome: | The proposed benchmarks show that the models fail under causal, structural, or functional constraints. |