8 citations · 12 across the 11 of their papers we have counts for
12 papers
Thinking Seeds: Restarting On-Policy RL from the Model's Own Past Reasoning States
Lei Yang, Wei Bi, Chenxi Sun +2
On-policy reinforcement learning (RL) for language models trains on the reasoning states visited by the current policy. As training sharpens the policy, some states reached by earl…
Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer
Yifan Zhang, Wei Bi, Kechi Zhang +3
Algorithm extraction aims to synthesize executable programs directly from models trained on algorithmic tasks, enabling de novo recovery of executable mechanisms from weights witho…
Open Multimodal Retrieval-Augmented Factual Image Generation
Yang Tian, Fan Liu, Jingyuan Zhang +3
Large Multimodal Models (LMMs) have achieved remarkable progress in generating photorealistic and prompt-aligned images, but they often produce outputs that contradict verifiable k…
v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound
Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou +6
AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions. To gauge and diagnose the capacity of multimoda…
PromCopilot: Simplifying Prometheus Metric Querying in Cloud Native Online Service Systems via Large Language Models
Chenxi Zhang, Bicheng Zhang, Dingyu Yang +6
With the increasing complexity of modern online service systems, understanding the state and behavior of the systems is essential for ensuring their reliability and stability. Ther…
FuseChat: Knowledge Fusion of Chat Models
Fanqi Wan, Longguang Zhong, Ziyi Yang +2
While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy…
Knowledge Fusion of Chat LLMs: A Preliminary Technical Report
Fanqi Wan, Ziyi Yang, Longguang Zhong +3
Recently, FuseLLM introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continu…
Knowledge Fusion of Large Language Models
Fanqi Wan, Xinting Huang, Deng Cai +3
While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant…
Knowledge Verification to Nip Hallucination in the Bud
Fanqi Wan, Xinting Huang, Leyang Cui +3
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible…
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Fanqi Wan, Xinting Huang, Tao Yang +3
Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks. However, existing data employed f…
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
Weizhou Shen, Yingqi Gao, Canbin Huang +3
Developing an efficient retriever to retrieve knowledge from a large-scale knowledge base (KB) is critical for task-oriented dialogue systems to effectively handle localized and sp…
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving
Xueliang Zhao, Xinting Huang, Wei Bi +1
Large Language Models (LLMs) have driven substantial progress in artificial intelligence in recent years, exhibiting impressive capabilities across a wide range of tasks, including…