From the 7 of 202 papers with an AI index.
53 citations
- Peking UniversityCN62 papers
- Tsinghua UniversityCN62 papers
- Institute of Modern PhysicsCN58 papers
- University of Science and Technology of ChinaCN57 papers
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT51 papers
- South China Normal UniversityCN51 papers
- University of TarapacáCL49 papers
- Zhejiang UniversityCN49 papers
- Beihang UniversityCN48 papers
- National Centre for Nuclear ResearchPL48 papers
- University of TurinIT47 papers
- Carnegie Mellon UniversityUS46 papers
6 papers · 1 filter
From Isolated Tasks to Structured Capabilities: A Multilayer Taxonomy for Large Language Models
Shixin Fang, Jiachen Wo, Wenjuan Qin +2
Large language model (LLM) evaluation spans diverse tasks and benchmarks, yet evidence remains organized around tasks rather than the capabilities they probe. This fragmentation li…
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
Xiaosong Han, Ke Chen, Xindi Dai +7
In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. Ho…
Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
Hangxuan Li, Renjun Jia, Xuezhang Wu +3
Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applications. We define feature engi…
Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation
Jiatong Li, Junxian Li, Weida Wang +6
Recently, Large Language Models (LLMs) have demonstrated great potential in natural language-driven molecule discovery. However, existing datasets and benchmarks for molecule-text…
Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents
Haorui He, Yupeng Li, Dacheng Wen +4
State-of-the-art single-agent claim verification methods struggle with complex claims that require nuanced analysis of multifaceted evidence. Inspired by real-world professional fa…
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
Junjie Ye, Yuming Yang, Yang Nan +7
Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). Howe…