Papers by Tiansheng Hu
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on simple attribution that retrieves textual evidence as references. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to generate reliable attributions. |
| Outcome: | The proposed benchmark evaluates the ability of LLMs to generate long-form answers with reliable and nuanced attributions. |
H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs (2026.eacl-long)
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| Challenge: | Existing approaches to align pre-trained LLMs with instructions for one property are difficult to fine-tune. |
| Approach: | They propose a mixture-of-experts-based fusion mechanism that models alignment as a controllable drift within the subspace, guided by a drift-regularization loss to balance competing alignment dimensions. |
| Outcome: | Extensive evaluations of three benchmark datasets show that H3Fusion outperforms each individually aligned model by 11.37% and provides stronger robustness compared to the state-of-the-art LLM ensemble approaches by 13.77% and model-merging approaches by 6.18 %. |
FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain (2025.emnlp-main)
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| Challenge: | Recent LLMs have demonstrated promising ability in solving finance related problems, but applying them in real-world finance applications remains challenging due to its high risk and high stakes property. |
| Approach: | They propose a benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. |
| Outcome: | The proposed benchmark outperforms proprietary models in most tasks while open-source models have advantage in specific areas like industry-level fairness. |
LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity (2024.findings-emnlp)
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| Challenge: | Extensive evaluation of modern large language models shows performance gain over component LLMs. |
| Approach: | They propose a diversityoptimized LLM ensemble method with three unique properties . they introduce the focal diversity metric to capture diversityperformance correlation . |
| Outcome: | The proposed method outperforms the best-performing ensemble on four benchmarks. |
SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature (2026.eacl-long)
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| Challenge: | Existing retrieval-augmented generation methods overlook citation graph structure, adapt poorly to complex queries, and yield fragmented, hard-to-verify syntheses. |
| Approach: | They propose a retrieval-augmented generation framework that addresses these gaps by combining adaptive retrieval and symbolic reasoning. |
| Outcome: | Extensive experiments show that SciRAG outperforms prior systems in factual accuracy and synthesis quality. |
Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026.acl-long)
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Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, Arman Cohan
| Challenge: | Recent advances in large language models (LLMs) motivated methods that assist or automate different stages of peer review pipeline. |
| Approach: | They synthesize techniques to enhance peer review generation and after-review tasks aligned to reviews. |
| Outcome: | The proposed methods improve the peer review process by fine-tuning strategies, agent-based systems, and emerging paradigms. |
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation (2026.findings-acl)
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| Challenge: | Prior studies show that large language models can draft fluent reviews but they miss specific issues, show shallow analysis, and produce generic phrasing. |
| Approach: | They propose a task that targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. |
| Outcome: | The proposed model improves on a large dataset that maps review segments to rebuttal segments that address them, with perspective labels and impact categories that order author uptake. |