Papers by Sijia Li

EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models (2024.findings-acl)

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Yuyan Chen, Songzhou Yan, Sijia Liu, Yueze Li, Yanghua Xiao

Challenge:	Existing evaluations of emotional intelligence in large language models (LLMs) focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence.
Approach:	They propose a framework for evaluating the emotional intelligence of large language models (LLMs) that includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition.
Outcome:	The proposed framework includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition.

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.

rSIM: Incentivizing Reasoning Capabilities of LLMs via Reinforced Strategy Injection (2026.acl-long)

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Sijia Chen, Baochun Li, Di Niu

Language Models

Challenge:	Existing literature on Reasoning Language Models (RLMs) focuses on the ability to integrate reasoning strategies into the chain-of-thought process, contributing to improved problem-solving accuracy.
Approach:	They propose a reinforced strategy injection mechanism that enables any LLM to become an RLM by employing a small planner to guide the LLM's CoT through the adaptive injection of reasoning strategies.
Outcome:	The proposed model outperforms existing models in mathematical, coding, and financial reasoning tasks and is generalizable.

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

Multimodality

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

GOBench: Stage-Wise Diagnostics and the Visual Paradox in Multimodal Graph Optimization (2026.findings-acl)

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Yinghao Chen, Wantong Xie, Shuli Zeng, Sijia Zhang, Xiaotian Pan, Feng Wu, Xiangyang Li

Multimodality

Challenge:	Existing benchmarks fail to represent multimodal problem specifications, score outcomes only and cannot localize where failures occur along the modeling pipeline.
Approach:	They propose a Graph Optimization benchmark that aligns multiple modalities with solver-derived oracles and a diagnostic protocol that evaluates intermediate artifacts as well as end results.
Outcome:	Graph Optimization benchmark (GOBench) evaluates intermediate artifacts as well as end results . vision reliably increases inference cost, while reliability impact is regime-dependent . current benchmarks fail to represent multimodal problem specifications, fail to localize failures .

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

Robustness in NLP

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).

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

Responsible & Trustworthy NLP

Challenge:	Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty.
Approach:	They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline.
Outcome:	The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets.

More Samples or More Prompts? Exploring Effective Few-Shot In-Context Learning for LLMs with In-Context Sampling (2024.findings-naacl)

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Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang

Low-Resource NLP

Challenge:	Existing studies on LLM prompting focus on selecting a better set of data samples inside one single prompt input, but why not design and leverage multiple ICL prompts together to further improve the LLM’s performance?
Approach:	They propose a low-resource LLM prompting technique to optimize the construction of multiple ICL prompt inputs to produce confident predictions.
Outcome:	The proposed technique can produce confident predictions by optimizing the construction of multiple ICL prompt inputs on four NLI datasets and one QA dataset.

Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios? (2024.findings-acl)

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Yuyan Chen, Yueze Li, Songzhou Yan, Sijia Liu, Jiaqing Liang, Yanghua Xiao

Semantic Text Processing

Challenge:	Existing games such as "Who is undercover" are subjective and difficult to evaluate .
Approach:	They propose a game called BrainKing that evaluates LLMs' problem-solving capability under incomplete information scenarios.
Outcome:	The proposed game requires LLMs to identify target entities with limited yes-or-no questions and potential misleading answers.

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

Multimodality

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

Annotate Chinese Aspect with UMR——a Case Study on the Liitle Prince (2024.lrec-main)

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Sijia Ge, Zilong Li, Alvin Po-Chun Chen, Guanchao Wang

Challenge:	Uniform Meaning Representation (UMR) is a graphbased cross-linguistically applicable semantic representation that allows for deep semantic analysis.
Approach:	They propose to use an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspect.
Outcome:	The proposed representations are based on the Chinese version of The Little Prince and are compared with other representations.

MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval (2024.findings-naacl)

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Youbo Lei, Feifei He, Chen Chen, Yingbin Mo, Sijia Li, Defeng Xie, Haonan Lu

Green & Sustainable NLP

Challenge:	Large-scale visual-language pretraining models have shown remarkable capabilities in understanding both vision and language.
Approach:	They propose a multi-teacher cross-modality alignment distillation technique to integrate the advantages of single-stream and dual-stream models.
Outcome:	The proposed model is lightweight and has only 100M running memory and 8.0ms search latency.

Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling (2026.findings-acl)

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Wantong Xie, Yinghao Chen, Yi-Xiang Hu, Feng Wu, Jieyang Xu, Sijia Zhang, Xiangyang Li

Responsible & Trustworthy NLP

Challenge:	Existing retrieval-augmented strategies for large language models fail to capture dynamic reasoning required to resolve execution failures.
Approach:	They propose a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge.
Outcome:	The proposed framework improves model accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%.