activity
20242026
collaborators

5 papers

cs.IR2026

From Item-Only to Query-Item: Query-Conditioned Generative Search with QGS in Quark

Yanglong Song, Zihao Yang, Shuo Meng +6

Generative sequence models have shown strong results in recommendation. Applying them to search ranking is more challenging. Search behavior is inherently query-driven. Each query…

cs.CV2026

HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation

Conglang Zhang, Yifan Zhan, Qingjie Wang +10

Closed-loop driving simulation requires real-time interaction beyond short offline clips, pushing current driving world models toward autoregressive (AR) rollout. Existing AR disti…

cs.CL2026

Compared to What? Baselines and Metrics for Counterfactual Prompting

Zihao Yang, Mosh Levy, Yoav Goldberg +1

Counterfactual prompting (i.e., perturbing a single factor and measuring output change) is widely used to evaluate things like LLM bias and CoT faithfulness. But in this work we ar…

cs.CL2025

BPQA Dataset: Evaluating How Well Language Models Leverage Blood Pressures to Answer Biomedical Questions

Chi Hang, Ruiqi Deng, Lavender Yao Jiang +4

Clinical measurements such as blood pressures and respiration rates are critical in diagnosing and monitoring patient outcomes. It is an important component of biomedical data, whi…

cs.CL2024

Refining Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models

Yanbing Chen, Ruilin Wang, Zihao Yang +2

Packing and shuffling tokens is a common practice in training auto-regressive language models (LMs) to prevent overfitting and improve efficiency. Typically documents are concatena…