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Found 5 papers, 3 papers with code
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content.
Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems.
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems.
Fine-grained Conversational Decoding via Isotropic and Proximal Search
General-purpose text decoding approaches are usually adopted for dialogue response generation.
