4 papers
Investigating More Explainable and Partition-Free Compositionality Estimation for LLMs: A Rule-Generation Perspective
Ziyao Xu, Cong Wang, Houfeng Wang
Compositional generalization tests are often used to estimate the compositionality of LLMs. However, such tests have the following limitations: (1) they only focus on the output re…
Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning
Shaobo Wang, Jiaming Wang, Jiajun Zhang +9
As supervised fine-tuning (SFT) evolves from a lightweight post-training step into a compute-intensive phase rivaling mid-training in scale, data efficiency has become critical for…
Diffusion LLM with Native Variable Generation Lengths: Let [EOS] Lead the Way
Yicun Yang, Cong Wang, Shaobo Wang +4
Diffusion-based large language models (dLLMs) have exhibited substantial potential for parallel text generation, which may enable more efficient generation compared to autoregressi…
Rethinking LLM Evaluation: Can We Evaluate LLMs with 200x Less Data?
Shaobo Wang, Cong Wang, Wenjie Fu +11
As the demand for comprehensive evaluations of diverse model capabilities steadily increases, benchmark suites have correspondingly grown significantly in scale. Despite notable ad…