2 citations · 4 across the 6 of their papers we have counts for
6 papers
ASCIIEval: Benchmarking Models' Visual Perception in Text Strings via ASCII Art
Qi Jia, Xiang Yue, Shanshan Huang +5
Perceiving visual semantics embedded within consecutive characters is a crucial yet under-explored capability for both Large Language Models (LLMs) and Multi-modal Large Language M…
Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation
Yizhu Liu, Ran Tao, Shengyu Guo +1
Topic relevance between query and document is a very important part of social search, which can evaluate the degree of matching between document and user's requirement. In most soc…
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
Qi Jia, Siyu Ren, Yizhu Liu +1
Despite tremendous improvements in natural language generation, summarization models still suffer from the unfaithfulness issue. Previous work evaluates faithfulness either using m…
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation
Qi Jia, Yizhu Liu, Haifeng Tang +1
Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rule…
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions
Qi Jia, Yizhu Liu, Siyu Ren +1
Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors. It has attracted grea…
Post-Training Dialogue Summarization using Pseudo-Paraphrasing
Qi Jia, Yizhu Liu, Haifeng Tang +1
Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models. These features eith…