30 citations · 101 across the 45 of their papers we have counts for
15 papers · 1 filter
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…
Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
Fengran Mo, Chen Qu, Kelong Mao +4
Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational sear…
How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
Fengran Mo, Longxiang Zhao, Kaiyu Huang +3
Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn i…
Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation
Qiwei Shao, Fengran Mo, Jian-Yun Nie
Document-level biomedical concept extraction is the task of identifying biomedical concepts mentioned in a given document. Recent advancements have adapted pre-trained language mod…
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution
Yulong Mao, Kaiyu Huang, Changhao Guan +3
Fine-tuning large-scale pre-trained models is inherently a resource-intensive task. While it can enhance the capabilities of the model, it also incurs substantial computational cos…
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
Yihong Wu, Le Zhang, Fengran Mo +3
Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their d…
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
Fengran Mo, Abbas Ghaddar, Kelong Mao +4
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous qu…
Language Modeling Using Tensor Trains
Zhan Su, Yuqin Zhou, Fengran Mo +1
We propose a novel tensor network language model based on the simplest tensor network (i.e., tensor trains), called `Tensor Train Language Model' (TTLM). TTLM represents sentences…
Mixture of Latent Experts Using Tensor Products
Zhan Su, Fengran Mo, Prayag Tiwari +3
In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with…
A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
Kaiyu Huang, Fengran Mo, Xinyu Zhang +10
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia…
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Kelong Mao, Chenlong Deng, Haonan Chen +4
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization…
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
Jiayin Wang, Fengran Mo, Weizhi Ma +3
Large language models (LLMs) are essential tools that users employ across various scenarios, so evaluating their performance and guiding users in selecting the suitable service is…
ConvSDG: Session Data Generation for Conversational Search
Fengran Mo, Bole Yi, Kelong Mao +3
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversa…
History-Aware Conversational Dense Retrieval
Fengran Mo, Chen Qu, Kelong Mao +4
Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehens…