7 citations · 14 across the 8 of their papers we have counts for
8 papers
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
Small-Margin Preferences Still Matter-If You Train Them Right
Jinlong Pang, Zhaowei Zhu, Na Di +4
Preference optimization methods such as DPO align large language models (LLMs) using paired comparisons, but their effectiveness can be highly sensitive to the quality and difficul…
RLPO: Residual Listwise Preference Optimization for Long-Context Review Ranking
Hao Jiang, Zhi Yang, Annan Wang +2
Review ranking is pivotal in e-commerce for prioritizing diagnostic and authentic feedback from the deluge of user-generated content. While large language models have improved sema…
Quantifying and Correcting Measurement Error in LLM-Generated Classifications: A Beta-Binomial Latent Class Model for Correlated Repeated Judgments
Yichi Zhang, Ignacio Martinez
Large language models are increasingly used to classify text at scale. Because their outputs are stochastic, it is common to query a model several times per item and aggregate the…
Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation
Yichi Zhang, Alexander Belloni, Ethan X. Fang +2
Motivated by the need for rigorous and scalable evaluation of large language models, we study contextual preference inference for pairwise comparison functionals of context-depende…
Bias Amplification: Large Language Models as Increasingly Biased Media
Ze Wang, Zekun Wu, Jeremy Zhang +5
Model collapse, a phenomenon characterized by performance degradation due to iterative training on synthetic data, has been widely studied. However, its implications for bias ampli…
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
Qingyang Wu, Ying Xu, Tingsong Xiao +8
Large Language Models (LLMs) have emerged as potent tools for advancing the United Nations' Sustainable Development Goals (SDGs). However, the attitudinal disparities between LLMs…