Papers by Tianyong Hao
A Self-supervised Joint Training Framework for Document Reranking (2022.findings-naacl)
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| Challenge: | Pretrained language models have been successfully applied to a wide range of tasks . however, the pretraining tasks were based on the context of documents . |
| Approach: | They propose a self-supervised joint training framework with a method called Masked Query Prediction to establish semantic relations between given queries and positive documents. |
| Outcome: | The proposed framework outperforms existing models on document reranking tasks without further pre-training . it uses a self-supervised method to establish semantic relations between given queries and positive documents. |
When Allies Turn Foes: Exploring Group Characteristics of LLM-Based Multi-Agent Collaborative Systems Under Adversarial Attacks (2025.findings-emnlp)
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| Challenge: | a new study examines the group characteristics of adversarial agents in multi-agent collaborative systems . collaborative agents are tasked with generating counterfactual answers to a given collaborative problem . |
| Approach: | They evaluate collaborative systems under adversarial attacks and propose methods to mitigate them . they also introduce a new metric to quantify the robustness of collaborative systems against such attacks . |
| Outcome: | The proposed method has been proven effective against adversarial attacks. |
LLM-Enhanced Query Generation and Retrieval Preservation for Task-Oriented Dialogue (2025.findings-acl)
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| Challenge: | Existing knowledge retrieval methods for task-oriented dialogues are limited by data scarcity and lack of data to annotate. |
| Approach: | They propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue . they propose to select the most relevant knowledge from retrieved top-K records and incorporate them as prompts to guide a generator in response generation. |
| Outcome: | The proposed model outperforms state-of-the-art in three benchmarks on three standard benchmarks. |
PolCLIP: A Unified Image-Text Word Sense Disambiguation Model via Generating Multimodal Complementary Representations (2024.acl-long)
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| Challenge: | Existing models for word sense disambiguation lack images or senses in textual and visual datasets. |
| Approach: | They propose a unified image-text WSD model that uses image-sense complementarity to generate visual representations for word senses and a disambiguation-oriented image-sensor dataset to provide implicit textual representations. |
| Outcome: | The proposed model achieves 2.53% F1-score increase over state-of-the-art models on Textual-WSD and 2.22% HR@1 improvement on Visual-WSS. |
Can Large Language Models Translate Spoken-Only Languages through International Phonetic Transcription? (2025.emnlp-main)
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| Challenge: | Existing research on spoken-only languages has focused on low-resource languages . spoken- only languages are among the most vulnerable to extinction . |
| Approach: | They propose a unified language understanding framework that learns to translate spoken-only languages via in-context learning. |
| Outcome: | The proposed framework can translate spoken-only languages into high-resource languages using phonetic transcription and automatic dictionary construction and knowledge retrieval. |
T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine (C18-2)
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| Challenge: | Traditional Chinese Medicine (TCM) is one of precious intangible cultural heritages of the Chinese nation. |
| Approach: | They propose to use authorized and anonymized clinical records, medicine clinical guidelines, teaching materials, classic medical books, academic publications, etc. as data resources to build a TCM knowledge graph. |
| Outcome: | The proposed system extracts triples from free texts to build a TCM knowledge graph. |