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Shuo Wu

2 accepted papers

2025

Hierachical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM

NeurIPS 2025poster

Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communic…

Cited by 0SourcecodeScholar
2025

Tool Playgrounds: A Comprehensive and Analyzable Benchmark for LLM Tool Invocation

ICASSP 2025accepted

The rapid advancement of large language models (LLMs) has paved the way for their use in solving real-world problems, which in turn has significantly driven the development of tool-assisted LLMs. This progress necessitates thorough evaluation methods. However, existing benchmarks typically only prov…

Cited by 0SourceScholar