Papers by Yiping Kang
One Agent To Rule Them All: Towards Multi-agent Conversational AI (2022.findings-acl)
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Christopher Clarke, Joseph Peper, Karthik Krishnamurthy, Walter Talamonti, Kevin Leach, Walter Lasecki, Yiping Kang, Lingjia Tang, Jason Mars
| Challenge: | Increasing volume of conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. |
| Approach: | They propose a task BBAI: Black-Box Agent Integration that integrates multiple black-box CAs at scale. |
| Outcome: | The proposed system outperforms existing benchmarks in the BBAI: Black-Box Agent Integration task. |
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG (2025.emnlp-industry)
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Savini Kashmira, Jayanaka L. Dantanarayana, Joshua Brodsky, Ashish Mahendra, Yiping Kang, Krisztian Flautner, Lingjia Tang, Jason Mars
| Challenge: | Retrieval-Augmented Generation (RAG) relies on query-chunk text-to-text similarity in the embedding space for retrieval, can fail to capture deeper semantic relationships across chunks, is highly sensitive to chunking strategies, and is prone to hallucinations. |
| Approach: | They propose a graph-based retrieval framework that first constructs the knowledge graph from unstructured data dynamically and automatically. |
| Outcome: | The proposed framework outperforms multiple RAG implementations in both precision and recall, significantly enhancing user experience through improved retrieval accuracy. |
Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Existing approaches to text classification assume a fixed set of labels . however, in real-world applications, there exists an infinite label space for describing a given text . |
| Approach: | They propose two new methods that inject aspect-level understanding into pre-trained models at train time to improve zero-shot generalization. |
| Outcome: | The proposed methods improve zero-shot generalization on a set of challenging datasets. |
Data Collection for Dialogue System: A Startup Perspective (N18-3)
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| Challenge: | Developing dialogue systems such as Apple Siri and Google Now requires high quality training data but data collection with crowdsourcing is largely an open question. |
| Approach: | They propose to use crowdsourcing to collect data for a user intent classification task in a dialogue system. |
| Outcome: | The proposed method improves the quality of the collected data and the model performance on real user queries. |