9 papers
RAMP: Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways
Dairui Liu, Zhongyi Lu, Roger Zhe Li +11
Click-through rate (CTR) and conversion rate (CVR) prediction are fundamental tasks in online advertising, aiming to estimate the likelihood of user interactions based on various f…
From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale
Changhong Jin, Shiqiu Yang, Roger Zhe Li +8
The paper surveys how industrial recommender systems have moved from using opaque raw identifiers to richer semantic IDs that embed item content and context, and proposes a future…
OGER: A Robust Offline-Guided Exploration Reward for Hybrid Reinforcement Learning
Xinyu Ma, Mingzhou Xu, Xuebo Liu +4
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have significantly improved Large Language Model (LLM) reasoning, yet models often struggle to explore…
LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection
Cheng Xu, Changhong Jin, Yingjie Niu +5
The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to complex reasoning. However, evaluati…
Relational Probing: LM-to-Graph Adaptation for Financial Prediction
Yingjie Niu, Changhong Jin, Rian Dolphin +1
Language models can be used to identify relationships between financial entities in text. However, while structured output mechanisms exist, prompting-based pipelines still incur a…
When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?
Xinyu Zhou, Chang Jin, Carsten Eickhoff +2
Large language models (LLMs) rarely admit uncertainty, often producing fluent but misleading answers, rather than abstaining (i.e., refusing to answer). This weakness is even evide…