Papers by Xinyan Zhao
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)
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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao
| Challenge: | Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes. |
| Approach: | They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior. |
| Outcome: | The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior. |
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking (2024.findings-emnlp)
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Zhuoer Wang, Leonardo Ribeiro, Alexandros Papangelis, Rohan Mukherjee, Tzu-Yen Wang, Xinyan Zhao, Arijit Biswas, James Caverlee, Angeliki Metallinou
| Challenge: | Existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user’s request. |
| Approach: | They propose an output-side optimization approach that uses state-tracked constrained decoding and a lightweight model as the discriminator to rerank the beam-searched candidate generations of the large language model. |
| Outcome: | The proposed approach outperforms existing supervised and in-context learning approaches in the DSTC8 and API Bank datasets in terms of call generation accuracy, inference efficiency, and context efficiency. |
SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention (2025.acl-long)
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| Challenge: | Content analysis is labor-intensive and time-consuming process that requires multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. |
| Approach: | They propose a multi-agent framework that effectively Simulates Content Analysis via Large language model (LLM) ag Ents. |
| Outcome: | The proposed framework achieves human-approximated performance across various content analysis tasks. |
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users (2023.findings-emnlp)
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Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
| Challenge: | a dataset of task-oriented dialogues assume conversations between the agent and one user at a time . but multi-user task-orientated dialogues are richer, containing deliberation and deliberations . a novel task is proposed to rewrite a task-focused query that retains only task-relevant information . |
| Approach: | They propose to rewrite a task-oriented chat between two users as a concise task-orientated query that retains only task-relevant information and is directly consumable by the dialogue system. |
| Outcome: | The proposed method surpasses existing models on multi-user dialogues and generalizes to unseen domains. |
PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions (2020.findings-emnlp)
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| Challenge: | a novel machine translation-based approach to simplify prescription directions is proposed . the language used by physicians and health professionals includes medical jargon and implicit directives . |
| Approach: | They propose a machine translation-based approach to automatically and reliably simplify prescription directions into patient-friendly language. |
| Outcome: | The proposed system achieves a BLEU score of 60.27 over 530K prescriptions from a large mail-order pharmacy. |
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition (2021.eacl-main)
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| Challenge: | Named entity recognition (NER) models often need to be trained with many manual labels to perform well. |
| Approach: | They propose to train named entity recognition systems using heuristic labeling rules . they create a graph with nodes representing candidate rules extracted from unlabeled data . |
| Outcome: | The proposed method achieves an average improvement of +20% over the baseline on three NER datasets. |
Narrative Style and the Spread of Health Misinformation on Twitter (2023.findings-emnlp)
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| Challenge: | Using a narrative style is an effective way to communicate health information on and off social media platforms. |
| Approach: | They annotate health misinformation tweets and classify them into narrative and non-narrative . they then use supervised fine-tuning and in-context learning to detect narratives . |
| Outcome: | The proposed model analyzes health misinformation tweets and finds that narrative use is linked to increased tweet engagement and can lead to increased misinformation use. |
Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation (2025.findings-acl)
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Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris
| Challenge: | Safety reasoning paradigms require high-quality policy-embedded chain-of-thought datasets . generating such data through human annotations is prohibitively expensive . |
| Approach: | They propose AIDSAFE: Agentic Iterative Deliberation for Safety Reasoning . AIDS AFE leverages multi-agent deliberation to iteratively expand reasoning on safety policies . |
| Outcome: | The proposed model improves policy adherence and reasoning quality while maintaining acceptable utility and over-refusal accuracy. |