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BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences
Ru Peng, Haokai Xu, Xijun Gu +11
While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS…
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
Ru Peng, Tianyu Zhao, Xijun Gu +9
High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the hum…
Training-Trajectory-Aware Token Selection
Zhanming Shen, Jiaqi Hu, Zeyu Qin +7
Efficient distillation is a key pathway for converting expensive reasoning capability into deployable efficiency, yet in the frontier regime where the student already has strong re…
HeartBench: Probing Core Dimensions of Anthropomorphic Intelligence in LLMs
Jiaxin Liu, Peiyi Tu, Wenyu Chen +9
While Large Language Models (LLMs) have achieved remarkable success in cognitive and reasoning benchmarks, they exhibit a persistent deficit in anthropomorphic intelligence-the cap…
Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation
Wenzhen Luo, Wei Guan, Yifan Yao +6
We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 database…
dInfer: An Efficient Inference Framework for Diffusion Language Models
Yuxin Ma, Lun Du, Lanning Wei +20
Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, leveraging denoising-based generation to enable inherent parallel…
LLaDA-MoE: A Sparse MoE Diffusion Language Model
Fengqi Zhu, Zebin You, Yipeng Xing +23
We introduce LLaDA-MoE, a large language diffusion model with the Mixture-of-Experts (MoE) architecture, trained from scratch on approximately 20T tokens. LLaDA-MoE achieves compet…
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
Haoyuan Wu, Haoxing Chen, Xiaodong Chen +10
The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse para…