16 citations · 25 across the 8 of their papers we have counts for
8 papers
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese
Yunqi Xu, Tianchi Cai, Jiyan Jiang +1
The prevailing issue of factual inconsistency errors in conventional Retrieval Augmented Generation (RAG) motivates the study of Factual Consistency Evaluation (FCE). Despite the v…
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering
Tianchi Cai, Zhiwen Tan, Xierui Song +5
Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form questio…
Mitigate Position Bias with Coupled Ranking Bias on CTR Prediction
Yao Zhao, Zhining Liu, Tianchi Cai +3
Position bias, i.e., users' preference of an item is affected by its placing position, is well studied in the recommender system literature. However, most existing methods ignore t…
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
Tianchi Cai, Xierui Song, Jiyan Jiang +3
Aligning language models to human expectations, e.g., being helpful and harmless, has become a pressing challenge for large language models. A typical alignment procedure consists…
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
Tianchi Cai, Jiyan Jiang, Wenpeng Zhang +7
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing b…
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Tianchi Cai, Shenliao Bao, Jiyan Jiang +5
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most…
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
Tianchi Cai, Wenpeng Zhang, Lihong Gu +2
Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that ca…
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
Tianchi Cai, Daxi Cheng, Chen Liang +6
A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B tes…