22 citations · 25 across the 8 of their papers we have counts for
6 papers · 1 filter
CTG: Conflict-aware, Composite, and Collaborative Controlled Text Generation
Yu Li, Zhe Yang, Yi Huang +2
Recent advancements in large language models (LLMs) have demonstrated remarkable text generation capabilities. However, controlling specific attributes of generated text remains ch…
MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation
Yu Li, Shenyu Zhang, Rui Wu +5
Recent advancements in generative Large Language Models(LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. Eval…
DEE: Dual-stage Explainable Evaluation Method for Text Generation
Shenyu Zhang, Yu Li, Rui Wu +4
Automatic methods for evaluating machine-generated texts hold significant importance due to the expanding applications of generative systems. Conventional methods tend to grapple w…
HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding
Rihui Jin, Yu Li, Guilin Qi +7
Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures.To…
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data
Dehai Min, Nan Hu, Rihui Jin +8
Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain specific data has attracted wide attention. However, domain data often exists in a hybrid format, in…
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family
Yiming Tan, Dehai Min, Yu Li +4
ChatGPT is a powerful large language model (LLM) that covers knowledge resources such as Wikipedia and supports natural language question answering using its own knowledge. Therefo…