26 citations · 77 across the 36 of their papers we have counts for
39 papers
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval
Yuchen Hui, Fengran Mo, Jia Ao Sun +3
Modern conversational information access systems continuously extract user information from conversations to build user profiles for personalized retrieval. As user profiles accumu…
Mitigating Popularity Bias in Recommendation with Global Listwise Learning and Progressive Bi-Weighting
Tianyu Zhu, Jiandong Ding, Yansong Shi +2
In recommender systems, user feedback typically follows a long-tail distribution, which leads many recommendation algorithms to exacerbate popularity bias by disproportionately fav…
Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning
Jia Ao Sun, Hao Yu, Fengran Mo +4
Knowledge graph question answering (KGQA) requires navigating from topic entities to an answer several relations away. Recent methods prompt a frontier LLM to explore the graph thr…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional work…
CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation
Rui Qi, Fengran Mo, Sijin Lu +3
A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). How…
Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models
Yuxing Tian, Fengran Mo, Zhiqi Huang +2
Large Language Models (LLMs) have recently been explored as fine-grained zero-shot re-rankers by leveraging attention signals to estimate document relevance. However, existing meth…
Towards Dynamic Dense Retrieval with Routing Strategy
Zhan Su, Fengran Mo, Jinghan Zhang +4
The \textit{de facto} paradigm for applying dense retrieval (DR) to new tasks involves fine-tuning a pre-trained model for a specific task. However, this paradigm has two significa…
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting
Yuxing Tian, Fengran Mo, Weixu Zhang +2
The strong capabilities of recent Large Language Models (LLMs) have made them highly effective for zero-shot re-ranking task. Attention-based re-ranking methods, which derive relev…
Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
Yuxing Tian, Yiyan Qi, Fengran Mo +3
Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervi…
OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG
Fengran Mo, Zhan Su, Yuchen Hui +6
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The qual…
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation
Rui Qi, Fengran Mo, Yufeng Chen +6
Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most e…
Measuring the Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models
Shuo Zhang, Fabrizio Gotti, Fengran Mo +1
Hallucination in large language models (LLMs) is a fundamental challenge, particularly in open-domain question answering. Prior work attempts to detect hallucination with model-int…
Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapters
Zhan Su, Fengran Mo, Jinghan Zhang +3
Parametric Retrieval-Augmented Generation (PRAG) is a RAG approach that integrates external knowledge directly into model parameters using a LoRA adapter, aiming at reducing the in…
FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
Yan Wang, Keyi Wang, Shanshan Yang +12
Going beyond simple text processing, financial auditing requires detecting semantic, structural, and numerical inconsistencies across large-scale disclosures. As financial reports…
Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs
Jia Ao Sun, Hao Yu, Fabrizio Gotti +6
Large language models (LLMs) augmented with knowledge graphs (KGs) offer a promising approach for knowledge-intensive reasoning. Central to this approach is the selection of approp…
Adaptive Personalized Conversational Information Retrieval
Fengran Mo, Yuchen Hui, Yuxing Tian +5
Personalized conversational information retrieval (CIR) systems aim to satisfy users' complex information needs through multi-turn interactions by considering user profiles. Howeve…
Tensorized Clustered LoRA Merging for Multi-Task Interference
Zhan Su, Fengran Mo, Guojun Liang +4
Despite the success of the monolithic dense paradigm of large language models (LLMs), the LoRA adapters offer an efficient solution by fine-tuning small task-specific modules and m…
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…
An Entity Linking Agent for Question Answering
Yajie Luo, Yihong Wu, Muzhi Li +5
Some Question Answering (QA) systems rely on knowledge bases (KBs) to provide accurate answers. Entity Linking (EL) plays a critical role in linking natural language mentions to KB…
Conversational Search: From Fundamentals to Frontiers in the LLM Era
Fengran Mo, Chuan Meng, Mohammad Aliannejadi +1
Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand t…
FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
Yan Wang, Lingfei Qian, Xueqing Peng +18
Accurate interpretation of numerical data in financial reports is critical for markets and regulators. Although XBRL (eXtensible Business Reporting Language) provides a standard fo…
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence
Long Yuan, Fengran Mo, Kaiyu Huang +6
The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types suc…
RALI@TREC iKAT 2024: Achieving Personalization via Retrieval Fusion in Conversational Search
Yuchen Hui, Fengran Mo, Milan Mao +1
The Recherche Appliquee en Linguistique Informatique (RALI) team participated in the 2024 TREC Interactive Knowledge Assistance (iKAT) Track. In personalized conversational search,…
A Survey of Conversational Search
Fengran Mo, Kelong Mao, Ziliang Zhao +7
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) te…