14 citations · 46 across the 28 of their papers we have counts for
28 papers
VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models
Zhiqi Huang, Vivek Datla, Zhichao Xu +3
Neural ranking models have become core components of modern information retrieval systems and important building blocks of AI systems such as retrieval-augmented generation (RAG) p…
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…
Defragmenting Language Models: An Interpretability-based Approach for Vocabulary Expansion
Maitrey Mehta, Nishant Subramani, Zhichao Xu +2
All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictate the cost and latency of access to contemporary…
TransactionGPT
Yingtong Dou, Zhimeng Jiang, Tianyi Zhang +26
We present TransactionGPT (TGPT), a foundation model for consumer transaction data within one of the world's largest payment networks. TGPT is designed to understand and generate t…
Reinforcement Mid-Training
Yijun Tian, Shaoyu Chen, Zhichao Xu +4
The development of state-of-the-art large language models is commonly understood as a two-stage process involving pre-training and post-training. We point out the need for an addit…
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…
Found in Translation: Measuring Multilingual LLM Consistency as Simple as Translate then Evaluate
Ashim Gupta, Maitrey Mehta, Zhichao Xu +1
Large language models (LLMs) provide detailed and impressive responses to queries in English. However, are they really consistent at responding to the same query in other languages…
SLOT: Structuring the Output of Large Language Models
Darren Yow-Bang Wang, Zhengyuan Shen, Soumya Smruti Mishra +3
Structured outputs are essential for large language models (LLMs) in critical applications like agents and information extraction. Despite their capabilities, LLMs often generate o…
A Systematic Survey of Automatic Prompt Optimization Techniques
Kiran Ramnath, Kang Zhou, Sheng Guan +18
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. Ho…
CSPLADE: Learned Sparse Retrieval with Causal Language Models
Zhichao Xu, Aosong Feng, Yijun Tian +2
In recent years, dense retrieval has been the focus of information retrieval (IR) research. While effective, dense retrieval produces uninterpretable dense vectors, and suffers fro…
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…
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation
Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh +8
Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information. Retrieva…
State Space Models are Strong Text Rerankers
Zhichao Xu, Jinghua Yan, Ashim Gupta +1
Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Am…
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression
Zhichao Xu, Ashim Gupta, Tao Li +2
Increasingly, model compression techniques enable large language models (LLMs) to be deployed in real-world applications. As a result of this momentum towards local deployment, com…
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…
In-Context Example Ordering Guided by Label Distributions
Zhichao Xu, Daniel Cohen, Bei Wang +1
By allowing models to predict without task-specific training, in-context learning (ICL) with pretrained LLMs has enormous potential in NLP. However, a number of problems persist in…
Multi-dimensional Evaluation of Empathetic Dialog Responses
Zhichao Xu, Jiepu Jiang
Empathy is critical for effective and satisfactory conversational communication. Prior efforts to measure conversational empathy mostly focus on expressed communicative intents --…
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…
FARA: Future-aware Ranking Algorithm for Fairness Optimization
Tao Yang, Zhichao Xu, Zhenduo Wang +1
Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, th…
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Zhichao Xu, Daniel Cohen
Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstrac…
Reward-free Policy Imitation Learning for Conversational Search
Zhenduo Wang, Zhichao Xu, Qingyao Ai
Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses a…
An In-depth Investigation of User Response Simulation for Conversational Search
Zhenduo Wang, Zhichao Xu, Qingyao Ai +1
Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve users' search needs through multi-turn natural language…
Counterfactual Editing for Search Result Explanation
Zhichao Xu, Hemank Lamba, Qingyao Ai +2
Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factu…