Papers by Sai Zhang
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)
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| Challenge: | Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns. |
| Approach: | They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD. |
| Outcome: | The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions . |
Have LLMs Reopened the Pandora’s Box of AI-Generated Fake News? (2025.naacl-long)
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Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo, Bonam Mingole, S. Shyam Sundar, Sarah Rajtmajer, Amulya Yadav
| Challenge: | Large language models (LLMs) are increasingly being used by fake news creators to generate deceptive and persuasive content at scale. |
| Approach: | They propose to use large language models to generate fake news at scale and to assess the ability of human annotators and AI models to detect it. |
| Outcome: | The results show that LLMs are 68% more effective at detecting real news than humans, compared to humans and AI models for fake news detection. |
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
| Approach: | They propose a framework for aspect-based sentiment analysis that uses annotated data to train ABSA models. |
| Outcome: | The proposed framework surpasses previous methods on benchmarks. |
AIGT: AI Generative Table Based on Prompt (2025.coling-main)
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| Challenge: | Tabular data is an essential resource for many fields, but current methods do not fully utilize the rich information available in tables. |
| Approach: | They propose a method that utilizes metadata information to generate tabular data . they propose long-token partitioning algorithms that enable AIGT to model tables of any scale . |
| Outcome: | The proposed approach achieves state-of-the-art on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system. |
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2025.coling-demos)
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| Challenge: | Recent advances in retrieval-augmented generation have demonstrated impressive performance on the question-answering task. |
| Approach: | They propose a retrieval-augmented generation framework that generates an initial text answer and retrieves multimodal data relevant to the snippets of the initial text. |
| Outcome: | The proposed framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications. |
Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs (2026.findings-acl)
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| Challenge: | Existing efforts to improve task accuracy or enrich COT generation are lacking in multimodal large language models. |
| Approach: | They propose a Faithful-First Reasoning, Planning, and Acting framework that evaluates faithfulness of intermediate reasoning and uses it to plan and execute faithfulness-aware actions during inference. |
| Outcome: | The proposed framework improves perceptual faithfulness by up to 24% over prompt-based and tool-augmented reasoning frameworks without degrading task accuracy. |