Papers by Tianjun Sun
Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)
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| Challenge: | Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume. |
| Approach: | They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data. |
| Outcome: | The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs. |
Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model (2026.findings-acl)
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| Challenge: | Existing methods for multi-interest analysis of users rely on heuristic assumptions . however, the granularity of raw generation of LLMs is agnostic, leading to overly fine or coarse interest grouping. |
| Approach: | They propose an LLM-driven adaptive and representative multi-interest modeling framework that exploits the agnostic granularity of LLMs for multi-interest analysis. |
| Outcome: | The proposed model outperforms baselines on real-world datasets. |
TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Existing communication topologies rely on spatio-temporal dialogues, which incur high latency and computation. |
| Approach: | They propose a framework for one-shot Topology generation with Diverse Interaction Modes that enables agents to construct heterogeneous communication without iterative coordination. |
| Outcome: | The proposed framework reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. |
Generative Personality Simulation via Theory-Informed Structured Interview (2026.eacl-long)
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Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao, Frederick L. Oswald
| Challenge: | Personality structured interviews are often lacking in advancing social science research. |
| Approach: | They propose a method to incorporate psychological insights into LLM simulations . they use a measure theory grounded evaluation procedure to evaluate reliability and validity . |
| Outcome: | The proposed method improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes. |