4 citations · 12 across the 21 of their papers we have counts for
9 papers · 1 filter
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin +6
OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.Op…
Case2Code: Scalable Synthetic Data for Code Generation
Yunfan Shao, Linyang Li, Yichuan Ma +11
Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs,…
Safe Inputs but Unsafe Output: Benchmarking Cross-modality Safety Alignment of Large Vision-Language Model
Siyin Wang, Xingsong Ye, Qinyuan Cheng +5
As Artificial General Intelligence (AGI) becomes increasingly integrated into various facets of human life, ensuring the safety and ethical alignment of such systems is paramount.…
Unified Active Retrieval for Retrieval Augmented Generation
Qinyuan Cheng, Xiaonan Li, Shimin Li +7
In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial…
SpeechAlign: Aligning Speech Generation to Human Preferences
Dong Zhang, Zhaowei Li, Shimin Li +4
Speech language models have significantly advanced in generating realistic speech, with neural codec language models standing out. However, the integration of human feedback to ali…
Agent Alignment in Evolving Social Norms
Shimin Li, Tianxiang Sun, Qinyuan Cheng +1
Agents based on Large Language Models (LLMs) are increasingly permeating various domains of human production and life, highlighting the importance of aligning them with human value…
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
Siyin Wang, Shimin Li, Tianxiang Sun +6
In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical questi…
Can AI Assistants Know What They Don't Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu +7
Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Alt…
SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
Dong Zhang, Xin Zhang, Jun Zhan +3
Benefiting from effective speech modeling, current Speech Large Language Models (SLLMs) have demonstrated exceptional capabilities in in-context speech generation and efficient gen…