3 papers
cs.CL2025
From Word to World: Can Large Language Models be Implicit Text-based World Models?
Yixia Li, Hongru Wang, Jiahao Qiu +7
Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World…
cs.CL2025
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs
Yan Yang, Yixia Li, Hongru Wang +4
With the proliferation of task-specific large language models, delta compression has emerged as a method to mitigate the resource challenges of deploying numerous such models by ef…
cs.CL2024
Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions
Yutao Hou, Yajing Luo, Zhiwen Ruan +4
Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, most benchmarks typic…