34 citations · 39 across the 24 of their papers we have counts for
28 papers
Not All Change Is Damage: Update-Matched Unlearning for Large Language Models
Jie Li, Youming Tao, Bowei He +1
Unlearning aims to remove selected knowledge from large language models to mitigate privacy and safety risks while preserving useful capabilities. For models that continue to be fi…
Trade-Adaptive Aggregation of Probabilistic Financial Forecasts
Yankai Chen, Rassul Magauin, Bowei He +5
Financial NLP systems produce probabilistic forecasts from news, reports, and filings. Prediction markets can aggregate these forecasts sequentially, but their fees must reward inf…
Prediction-Market Seed Capital Recovery from Noise-Dominant Flow
Yankai Chen, Bowei He, Zhuohan Xie +6
Automated prediction markets require sponsors to prefund liquidity before observing order flow, creating a financing challenge at launch. We study whether nonnegative charges condi…
AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction
Ziqiang Cui, Han Shi, Bowei He +8
Multi-Token Prediction (MTP) has emerged as an effective paradigm that augments a shared Large Language Model backbone with auxiliary heads, training the model to predict several f…
Restoring Collaborative Signals in Semantic-ID Generative Recommendation via Personalized Natural Language
Changjiang Han, Qingyang Li, Yaqiang Zang +4
Making LLM-based generative recommendation models stronger and more personalized through natural language and explicit reasoning is a widely anticipated yet still unsolved goal. Su…
Beyong Tokens: Item-aware Attention for LLM-based Recommendation
Xiaokun Zhang, Bowei He, Jiamin Chen +2
Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and…
Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting
Arina Kharlamova, Bowei He, Chen Ma +1
We present DANCEMATCH, an end-to-end framework for motion-based dance retrieval, the task of identifying semantically similar choreographies directly from raw video, defined as DAN…
Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation
Bowei He, Yankai Chen, Xiaokun Zhang +4
Knowledge distillation from Large Language Models (LLMs) to smaller models has emerged as a critical technique for deploying efficient AI systems. However, current methods for dist…
Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration
Bowei He, Minda Hu, Zenan Xu +7
Search-integrated reasoning enables language agents to transcend static parametric knowledge by actively querying external sources. However, training these agents via reinforcement…
Reasoning in Action: MCTS-Driven Knowledge Retrieval for Large Language Models
Shuqi Liu, Bowei He, Chen Ma +1
Large language models (LLMs) typically enhance their performance through either the retrieval of semantically similar information or the improvement of their reasoning capabilities…
Have We Really Understood Collaborative Information? An Empirical Investigation
Xiaokun Zhang, Zhaochun Ren, Bowei He +2
Collaborative information serves as the cornerstone of recommender systems which typically focus on capturing it from user-item interactions to deliver personalized services. Howev…
SDrug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening
Bowei He, Bowen Gao, Yankai Chen +5
Virtual screening (VS) is an essential task in drug discovery, focusing on the identification of small-molecule ligands that bind to specific protein pockets. Existing deep learnin…
RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
Chunyu Miao, Henry Peng Zou, Yangning Li +28
Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing…
Spatial CAPTCHA: Generatively Benchmarking Spatial Reasoning for Human-Machine Differentiation
Arina Kharlamova, Bowei He, Chen Ma +1
Online services rely on CAPTCHAs as a first line of defense against automated abuse, yet recent advances in multi-modal large language models (MLLMs) have eroded the effectiveness…
Less Is More: Elevating RAG via Performance-Driven Context Compression
Ziqiang Cui, Yunpeng Weng, Xing Tang +7
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for improving the timeliness of knowledge updates and the factual accuracy of large language models. Howeve…
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Yansen Zhang, Bowei He, Xiaokun Zhang +3
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent eff…
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
Xiaokun Zhang, Bo Xu, Chenliang Li +4
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts in…
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Yunpeng Weng, Xing Tang +8
Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive p…
Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
Renqi Jia, Xiaokun Zhang, Bowei He +4
User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods…
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks
Bowei He, Lihao Yin, Hui-Ling Zhen +4
The widespread deployment of pre-trained language models (PLMs) has exposed them to textual backdoor attacks, particularly those planted during the pre-training stage. These attack…
NILE: Internal Consistency Alignment in Large Language Models
Minda Hu, Qiyuan Zhang, Yufei Wang +7
As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain…
Interpretable Triplet Importance for Personalized Ranking
Bowei He, Chen Ma
Personalized item ranking has been a crucial component contributing to the performance of recommender systems. As a representative approach, pairwise ranking directly optimizes the…
Mitigating Large Language Model Hallucination with Faithful Finetuning
Minda Hu, Bowei He, Yufei Wang +3
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks. However, they are prone to generating fluent yet untruthful resp…
Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
Bowei He, Yunpeng Weng, Xing Tang +5
Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individ…