activity
20202026
most citedHuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

265 citations · 360 across the 67 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.IR2024

GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation

Fei Tang, Yongliang Shen, Hang Zhang +6

Large language model-based explainable recommendation (LLM-based ER) systems show promise in generating human-like explanations for recommendations. However, they face challenges i…

cs.CL2024★ 1 cited

EgoSocialArena: Benchmarking the Social Intelligence of Large Language Models from a First-person Perspective

Guiyang Hou, Wenqi Zhang, Yongliang Shen +3

Social intelligence is built upon three foundational pillars: cognitive intelligence, situational intelligence, and behavioral intelligence. As large language models (LLMs) become…

cs.CL2024

RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU

Chengyuan Liu, Shihang Wang, Fubang Zhao +5

Information Extraction (IE) and Text Classification (CLS) serve as the fundamental pillars of NLU, with both disciplines relying on analyzing input sequences to categorize outputs…

cs.CV2024

Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model

Wenqi Zhang, Zhenglin Cheng, Yuanyu He +8

Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or l…

cs.CL2024

TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models' Theory-of-Mind

Guiyang Hou, Wenqi Zhang, Yongliang Shen +2

Theory of Mind (ToM)-the cognitive ability to reason about mental states of ourselves and others, is the foundation of social interaction. Although ToM comes naturally to humans, i…

cs.CL2024

Advancing Process Verification for Large Language Models via Tree-Based Preference Learning

Mingqian He, Yongliang Shen, Wenqi Zhang +2

Large Language Models (LLMs) have demonstrated remarkable potential in handling complex reasoning tasks by generating step-by-step rationales.Some methods have proven effective in…