4 citations · 7 across the 6 of their papers we have counts for
6 papers
Toward Embodied AGI: A Review of Embodied AI and the Road Ahead
Yequan Wang, Aixin Sun
Artificial General Intelligence (AGI) is often envisioned as inherently embodied. With recent advances in robotics and foundational AI models, we stand at the threshold of a new er…
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Li Du, Yequan Wang, Xingrun Xing +4
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs. T…
FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li, Yiqun Yao, Xin Jiang +10
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
Yiqun Yao, Siqi fan, Xiusheng Huang +10
As language models scale up, it becomes increasingly expensive to verify research ideas because conclusions on small models do not trivially transfer to large ones. A possible solu…
Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis
Yequan Wang, Xuying Meng, Yiyi Liu +4
Many studies on dialog emotion analysis focus on utterance-level emotion only. These models hence are not optimized for dialog-level emotion detection, i.e. to predict the emotion…
Aspect-based Sentiment Analysis through EDU-level Attentions
Ting Lin, Aixin Sun, Yequan Wang
A sentence may express sentiments on multiple aspects. When these aspects are associated with different sentiment polarities, a model's accuracy is often adversely affected. We obs…