This survey aims to systematically address resource efficiency challenges of Large Language Models by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs, and introduces a nuanced categorization of resource efficiency techniques by their specific resource types.
This work addresses the credit-assignment gap in SID recommendation with retrieval-grounded query attribution, and reveals the potential of interest-conditioned SID decoding: selecting the target-relevant query among generated interests improves both recall and ranking.
Meng-Dan Zhu, Yu-Fan Zhao, Yao Zhao et al.· 0 citations
Evo-Rec is proposed, a three-stage framework for learning better reasoning and further enhancing it through reinforcement learning that consistently outperforms discriminative, generative, and reasoning-enhanced recommenders across all evaluation metrics.
Meng-Dan Zhu, Yu-Fan Zhao, Sophie Di et al.· 0 citations
This survey aims to systematically address resource efficiency challenges in Large Language Models by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs, and introduces a nuanced categorization of resource efficiency techniques by their specific resource types.
DLR is proposed, a reinforced latent reasoning framework that dynamically decomposes queries into textual premises, extracts premise-conditioned continuous visual latents, and deduces answers through grounded rationales to enable effective exploration in the latent space.
