8 papers
MoEGen: Mixture-of-Experts for Instance-Adaptive LoRA Generation
Yiming Zeng, Lei Lu, Zexin Li +9
Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve capacity by storing multiple ful…
ADVENT: LLM-Driven Automatic Predicate Invention for ILP
Tingting Yu, Pei-Cing Huang, Chan Hsu +2
Predicate invention (PI), the creation of new predicates to extend the hypothesis space, remains a critical bottleneck in Inductive Logic Programming (ILP). Existing methods rely o…
Latent Confidence Alignment for LLM Self-Assessment
Ting-Yu Chen, Tingting Yu, Pei-Cing Huang +3
Confidence calibration in large language models (LLMs) is commonly evaluated by comparing predicted confidence with observed accuracy. However, such approaches do not model item di…
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
QuarkMedBench: A Real-World Scenario Driven Benchmark for Evaluating Large Language Models
Yao Wu, Kangping Yin, Liang Dong +13
While Large Language Models (LLMs) excel on standardized medical exams, high scores often fail to translate to high-quality responses for real-world medical queries. Current evalua…
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications
Yiming Zeng, Wanhao Yu, Zexin Li +5
Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoni…