62 citations · 153 across the 36 of their papers we have counts for
6 papers · 1 filter
ConsintBench: Evaluating Language Models on Real-World Consumer Intent Understanding
Xiaozhe Li, TianYi Lyu, Siyi Yang +6
Understanding human intent is a complex, high-level task for large language models (LLMs), requiring analytical reasoning, contextual interpretation, dynamic information aggregatio…
NP-Engine: Empowering Optimization Reasoning in Large Language Models with Verifiable Synthetic NP Problems
Xiaozhe Li, Xinyu Fang, Shengyuan Ding +4
Large Language Models (LLMs) have shown strong reasoning capabilities, with models like OpenAI's O-series and DeepSeek R1 excelling at tasks such as mathematics, coding, logic, and…
OPT-BENCH: Evaluating LLM Agent on Large-Scale Search Spaces Optimization Problems
Xiaozhe Li, Jixuan Chen, Xinyu Fang +4
Large Language Models (LLMs) have shown remarkable capabilities in solving diverse tasks. However, their proficiency in iteratively optimizing complex solutions through learning fr…
Beyond Static Scenes: Camera-controllable Background Generation for Human Motion
Mingshuai Yao, Mengting Chen, Qinye Zhou +9
In this paper, we investigate the generation of new video backgrounds given a human foreground video, a camera pose, and a reference scene image. This task presents three key chall…
Squeeze Out Tokens from Sample for Finer-Grained Data Governance
Weixiong Lin, Chen Ju, Haicheng Wang +8
Widely observed data scaling laws, in which error falls off as a power of the training size, demonstrate the diminishing returns of unselective data expansion. Hence, data governan…
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…