11 papers
Improve Large Language Model Systems with User Logs
Changyue Wang, Weihang Su, Qingyao Ai +4
Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality d…
Decoupled Mixture-of-Experts for Parametric Knowledge Injection
Baoqing Yue, Weihang Su, Qingyao Ai +5
Knowledge injection aims to equip large language models (LLMs) with external, domain-specific, or time-sensitive knowledge. Existing approaches typically face a trade-off between f…
Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
Changyue Wang, Weihang Su, Qingyao Ai +5
Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inj…
Skill Retrieval Augmentation for Agentic AI
Weihang Su, Jianming Long, Qingyao Ai +6
As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities…
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
Qingyao Ai, Yichen Tang, Changyue Wang +3
Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual…
Towards Unification of Hallucination Detection and Fact Verification for Large Language Models
Weihang Su, Jianming Long, Changyue Wang +5
Large Language Models (LLMs) frequently exhibit hallucinations, generating content that appears fluent and coherent but is factually incorrect. Such errors undermine trust and hind…