14 papers
Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing
Yiming Liu, Ziyue Zhang, Zhichao Xu +4
Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse parsing (DDP) shows that sup…
A Survey of Model Architectures in Information Retrieval
Zhichao Xu, Fengran Mo, Zhiqi Huang +5
The period from 2019 to the present marks one of the most significant paradigm shifts in information retrieval (IR) and natural language processing (NLP), culminating in the emerge…
RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
Zhichao Xu
Transformer structure has achieved great success in multiple applied machine learning communities, such as natural language processing (NLP), computer vision (CV) and information r…
Context-aware Decoding Reduces Hallucination in Query-focused Summarization
Zhichao Xu
Query-focused summarization (QFS) aims to provide a summary of a single document/multi documents that can satisfy the information needs of a given query. It is useful for various r…
ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval
Fengran Mo, Jinghan Zhang, Yuchen Hui +4
Conversational search aims to satisfy users' complex information needs via multiple-turn interactions. The key challenge lies in revealing real users' search intent from the contex…
Distillation versus Contrastive Learning: How to Train Your Rerankers
Zhichao Xu, Zhiqi Huang, Shengyao Zhuang +1
Training effective text rerankers is crucial for information retrieval. Two strategies are widely used: contrastive learning (optimizing directly on ground-truth labels) and knowle…