4 citations · 10 across the 16 of their papers we have counts for
17 papers
Beyond Binary Labels: Diagnostic Evaluation of LLM Safety with Prompt-Level Rubrics
Hongyu Li, Xinlong Huang, Zhan Yu +13
Current LLM safety benchmarks predominantly rely on binary safe or unsafe labels, lacking explanatory depth and suffering from low consistency across automated judges. To address t…
Hunyuan-A13B Technical Report
Tencent Hunyuan Team, Ao Liu, Botong Zhou +72
We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion durin…
UltraLogic: Enhancing LLM Reasoning through Large-Scale Data Synthesis and Bipolar Float Reward
Yile Liu, Yixian Liu, Zongwei Li +7
While Large Language Models (LLMs) have demonstrated significant potential in natural language processing , complex general-purpose reasoning requiring multi-step logic, planning,…
Zero Reinforcement Learning Towards General Domains
Yuyuan Zeng, Yufei Huang, Can Xu +5
Zero Reinforcement Learning (Zero-RL) has proven to be an effective approach for enhancing the reasoning capabilities of large language models (LLMs) by directly applying reinforce…
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
Jason Chou, Ao Liu, Yuchi Deng +13
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have…
Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Zenan Xu, Zexuan Qiu, Guanhua Huang +9
Recent advances in large language models (LLMs) have accelerated progress toward artificial general intelligence, with inference-time scaling emerging as a key technique. Contempor…
ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation
Chenchen Zhang, Yuhang Li, Can Xu +17
The generative capabilities of Large Language Models (LLMs) are rapidly expanding from static code to dynamic, interactive visual artifacts. This progress is bottlenecked by a crit…
Adaptive Deep Reasoning: Triggering Deep Thinking When Needed
Yunhao Wang, Yuhao Zhang, Tinghao Yu +3
Large language models (LLMs) have shown impressive capabilities in handling complex tasks through long-chain reasoning. However, the extensive reasoning steps involved can signific…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data
Zihui Gu, Xingwu Sun, Fengzong Lian +3
Instruction-following is particularly crucial for large language models (LLMs) to support diverse user requests. While existing work has made progress in aligning LLMs with human p…
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
Jiazhen Liu, Yuhan Fu, Ruobing Xie +5
Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hall…
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao +4
Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulne…
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language
Zekai Qu, Ruobing Xie, Chaojun Xiao +5
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual in…
TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval
Kaibin Tian, Ruixiang Zhao, Hu Hu +4
For text-to-video retrieval (T2VR), which aims to retrieve unlabeled videos by ad-hoc textual queries, CLIP-based methods are dominating. Compared to CLIP4Clip which is efficient a…
BagFormer: Better Cross-Modal Retrieval via bag-wise interaction
Haowen Hou, Xiaopeng Yan, Yigeng Zhang +2
In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput. In this paper, w…
Global Sensing and Measurements Reuse for Image Compressed Sensing
Zi-En Fan, Feng Lian, Jia-Ni Quan
Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, ex…
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
Xu Li, Jingwen Wang, Lin Ma +4
Effective and Efficient spatio-temporal modeling is essential for action recognition. Existing methods suffer from the trade-off between model performance and model complexity. In…