1 citations · 4 across the 17 of their papers we have counts for
8 papers · 1 filter
Holdout-Loss-Based Data Selection for LLM Finetuning via In-Context Learning
Ling Zhang, Xianliang Yang, Juwon Yu +4
Fine-tuning large pretrained language models is a common approach for aligning them with human preferences, but noisy or off-target examples can dilute supervision. While small, we…
In-Context Compositional Q-Learning for Offline Reinforcement Learning
Qiushui Xu, Yuhao Huang, Yushu Jiang +4
Accurate estimation of the Q-function is a central challenge in offline reinforcement learning. However, existing approaches often rely on a shared global Q-function, which is inad…
Sample-efficient LLM Optimization with Reset Replay
Zichuan Liu, Jinyu Wang, Lei Song +1
Recent advancements in LLM post-training, particularly through reinforcement learning and preference optimization, are key to boosting their reasoning capabilities. However, these…
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Yiyuan Yang, Zichuan Liu, Lei Song +6
Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for trans…
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges
Xianliang Yang, Ling Zhang, Haolong Qian +2
Heuristic algorithms play a vital role in solving combinatorial optimization (CO) problems, yet traditional designs depend heavily on manual expertise and struggle to generalize ac…
Learning to Select In-Context Demonstration Preferred by Large Language Model
Zheng Zhang, Shaocheng Lan, Lei Song +3
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations. However, ICL performance is highly dependent…
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval
Wei Yang, Jingjing Fu, Rui Wang +3
Vision-language retrieval-augmented generation (RAG) has become an effective approach for tackling Knowledge-Based Visual Question Answering (KB-VQA), which requires external knowl…
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
Jinyu Wang, Jingjing Fu, Rui Wang +2
Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often str…