Papers by Shiyu Ji
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query (2025.emnlp-main)
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| Challenge: | Existing KV cache eviction methods prune tokens using prefilling-stage attention scores, causing inconsistency with actual inference queries. |
| Approach: | They propose a lookahead q-cache framework that generates low-cost pseudo lookaheaded queries to better approximate the true decoding-stage queries. |
| Outcome: | The proposed framework outperforms existing methods on LongBench and Needle-in-a-Haystack benchmarks and can be flexibly combined to yield further improvements. |
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025.findings-emnlp)
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Yujian Liu, Jiabao Ji, Tong Yu, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang
| Challenge: | Existing methods to integrate external information into a given table neglect the structured nature of the table. |
| Approach: | They propose a simple yet effective method to integrate external information into a given table by first building an augmenting table and then generating a SQL query over the two tables to answer the question. |
| Outcome: | The proposed method outperforms strong baselines on three table QA benchmarks. |
PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)
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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu
| Challenge: | PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints. |
| Approach: | They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly. |
| Outcome: | The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives. |
Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics. |
| Approach: | They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency. |
| Outcome: | The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss. |
A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling (2025.findings-emnlp)
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Shiyu Ji, Farnoosh Hashemi, Joice Chen, Juanwen Pan, Weicheng Ma, Hefan Zhang, Sophia Pan, Ming Cheng, Shubham Mohole, Saeed Hassanpour, Soroush Vosoughi, Michael Macy
| Challenge: | Rhetorical strategies are important to persuasive communication, but their analysis relies on human annotation, which is costly, inconsistent and difficult to scale. |
| Approach: | They propose a framework that leverages large language models to generate and label debate data . they fine-tune transformer-based classifiers on this dataset and validate it against human data a . |
| Outcome: | The proposed model achieves high performance and strong generalization across topical domains. |
Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025.findings-emnlp)
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Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Existing approaches to persuasive dialogue generation suffer from stance oscillation and low informativeness. |
| Approach: | They propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. |
| Outcome: | The proposed method ensures speaker characteristics guide all stages of dialogue generation and aligns language use with speakers’ native languages to better capture cultural nuances. |
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing (2024.naacl-short)
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Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang
| Challenge: | Existing adversarial attacks can cause LLMs to make wrong predictions on downstream tasks or generate harmful content misaligned with human values. |
| Approach: | They propose to use randomized smoothing to add noise to the input and then make predictions based on these denoised versions. |
| Outcome: | The proposed method surpasses existing methods in both empirical and certified robustness in defending against adversarial perturbations for both downstream tasks and human alignments (i.e., jailbreak attacks). |
Communication Makes Perfect: Persuasion Dataset Construction via Multi-LLM Communication (2025.naacl-long)
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Weicheng Ma, Hefan Zhang, Ivory Yang, Shiyu Ji, Joice Chen, Farnoosh Hashemi, Shubham Mohole, Ethan Gearey, Michael Macy, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. |
| Approach: | They propose a multi-LLM communication framework that facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. |
| Outcome: | The proposed framework excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. |