Papers by Qing Shi
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)
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| Challenge: | Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information. |
| Approach: | They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods . |
| Outcome: | The proposed method improves stock trend prediction and financial question answering tasks. |
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (2025.coling-main)
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| Challenge: | E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications . |
| Approach: | They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding . |
| Outcome: | The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. |
Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)
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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li
| Challenge: | Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. |
| Approach: | They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques. |
| Outcome: | The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs. |
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)
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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences . |
| Approach: | They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification . |
| Outcome: | The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization. |
Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning (2024.emnlp-main)
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| Challenge: | Existing models for Temporal Knowledge Graph reasoning capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
| Approach: | They propose a multi-granularity history and entity similarity learning model which captures the similarity between entities. |
| Outcome: | The proposed model can predict unknown facts based on historical information, but most existing models ignore multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. |
| Approach: | They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting. |
| Outcome: | The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations. |
GraDaSE: Graph-Based Dataset Search with Examples (2025.emnlp-main)
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| Challenge: | Existing methods address ad hoc dataset search, but dataset search presents in diverse and complex forms. |
| Approach: | They propose a graph-based approach to retrieve relevant datasets from textual queries . they identify provenance-based and topic-based relationships to construct a diagram . |
| Outcome: | The proposed approach outperforms strong baselines on two test collections. |
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)
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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi
| Challenge: | LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored . |
| Approach: | They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate . |
| Outcome: | The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios. |
SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains. |
| Approach: | They propose a collaborative framework that combines large and small models for effective reasoning. |
| Outcome: | The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference. |