Papers by Siyuan Song
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)
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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni
| Challenge: | Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging . |
| Approach: | They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks. |
| Outcome: | The proposed framework bridges the domain gap between LLMs and recommendation tasks. |
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better (2026.findings-acl)
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| Challenge: | Typical large vision-language models emphasize vision-to-language alignment while overlooking fine-grained visual information. |
| Approach: | They introduce autoregressive semantic visual reconstruction (ASVR) that enables joint learning of visual and textual modalities within a unified autoregression framework. |
| Outcome: | The proposed model improves baselines and multimodal understanding benchmarks by 2-3%. |
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction (2025.findings-naacl)
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ShengbinYue ShengbinYue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large Language Models (LLMs) have advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. |
| Approach: | They propose a Multi-agent Legal Simulation Driver to generate synthetic data by simulating interactive legal scenarios. |
| Outcome: | The proposed framework ensures consistency of legal attributes between participants and introduces a supervisory mechanism to align participants’ characters and behaviors as well as addressing distractions. |
A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)
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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu
| Challenge: | Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters. |
| Approach: | They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases. |
| Outcome: | The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters . |
Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments (2026.acl-long)
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| Challenge: | Existing benchmarks for legal intelligence are limited to static evaluation paradigms or simplified scenarios. |
| Approach: | They introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. |
| Outcome: | The proposed framework assesses task performance and procedural compliance across legal proficiency levels. |
Bears, all bears, and some bears. Language Constraints on Language Models’ Inductive Inferences (2026.findings-acl)
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| Challenge: | Language places subtle constraints on how we make inductive inferences. |
| Approach: | They propose to use language to constrain inductive inferences by replicating an experiment . they find subtle differences arise in general purpose statistical learners like VLMs . |
| Outcome: | The proposed model can be used to extend inductive inferences to humans using language . the model can extend properties of a category to other members of the population, the authors show . |
What Can String Probability Tell Us About Grammaticality? (2026.tacl-1)
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| Challenge: | linguistic theories have argued that language models have largely achieved grammatical competence, but they will assign non-zero probability to all strings. |
| Approach: | They propose a theoretical framework for analyzing string probabilities in linguistics based on simple assumptions about the generative process of corpus data. |
| Outcome: | The proposed framework makes three predictions using 280K sentence pairs in English and Chinese. |
Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)
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Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang
| Challenge: | Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents. |
| Approach: | They propose a cognitive framework that incorporates first-order and second-order perspective transitions into LLMs to enhance their ability to identify and counteract deceptive information. |
| Outcome: | The proposed framework enhances LLMs’ ability to identify and counteract deceptive information without extra fine-tuning and data. |
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data (2026.eacl-long)
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Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck, Bhargav Shandilya, Diana Galvan-Sosa, Faiz Ghifari Haznitrama, Francesca Padovani, Francois Meyer, Hai Hu, Julen Etxaniz, Laurent Prevot, Linyang He, María Grandury, Mila Marcheva, Negar Foroutan, Nikitas Theodoropoulos, Pouya Sadeghi, Siyuan Song, Suchir Salhan, Susana Zhou, Yurii Paniv, Ziyin Zhang, Arianna Bisazza, Alex Warstadt, Leshem Choshen
| Challenge: | prevailing trend in language modeling research is to prioritize scaling, authors say . from infancy to maturity, English learners acquire language through exposure to less than 100M words . |
| Approach: | They propose a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. |
| Outcome: | The proposed models outperform models trained on a fixed, developmentally plausible English corpus on various benchmarks. |