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Rank Aggregation in Crowdsourcing for Listwise Annotations
Wenshui Luo, Haoyu Liu, Yongliang Ding +7
Rank aggregation through crowdsourcing has recently gained significant attention, particularly in the context of listwise ranking annotations. However, existing methods primarily f…
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
Huanshuo Liu, Hao Zhang, Zhijiang Guo +6
Retrieval-augmented generation (RAG) has emerged as a promising solution for mitigating hallucinations of large language models (LLMs) with retrieved external knowledge. Adaptive R…
Planning with Multi-Constraints via Collaborative Language Agents
Cong Zhang, Derrick Goh Xin Deik, Dexun Li +2
The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) hav…
Multi-view Content-aware Indexing for Long Document Retrieval
Kuicai Dong, Derrick Goh Xin Deik, Yi Quan Lee +4
Long document question answering (DocQA) aims to answer questions from long documents over 10k words. They usually contain content structures such as sections, sub-sections, and pa…
Aligning Crowd Feedback via Distributional Preference Reward Modeling
Dexun Li, Cong Zhang, Kuicai Dong +3
Deep Reinforcement Learning is widely used for aligning Large Language Models (LLM) with human preference. However, the conventional reward modelling is predominantly dependent on…