97 citations · 101 across the 11 of their papers we have counts for
12 papers
Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts
Bo Wang, Heyan Huang, Yaolin Li +5
Retrieval-augmented generation (RAG) improves language models by grounding generation in external context. However, it can be fragile when the retrieved context conflicts with the…
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Guoqing Ma, Haoyang Huang, Kun Yan +112
We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length. A deep compression…
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning
Hao Sun, Jiayi Wu, Hengyi Cai +6
Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality and deploying local-based LLMs f…
Retrieved In-Context Principles from Previous Mistakes
Hao Sun, Yong Jiang, Bo Wang +4
In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to…
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement
Jiahao Ying, Mingbao Lin, Yixin Cao +5
This paper introduces the innovative "LLMs-as-Instructors" framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller targ…
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
Wei Tang, Yixin Cao, Jiahao Ying +4
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance,…
EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain
Yi-Fan Lu, Xian-Ling Mao, Bo Wang +2
It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event e…
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism
Bo Wang, Heyan Huang, Yixin Cao +3
While large language models (LLMs) have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanism offers…
Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models
Jiahao Ying, Yixin Cao, Yushi Bai +7
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for large…
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
Hao Sun, Hengyi Cai, Bo Wang +5
Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also know…
Boosting Event Extraction with Denoised Structure-to-Text Augmentation
bo wang, Heyan Huang, Xiaochi Wei +6
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving…
Dynamic Prefix-Tuning for Generative Template-based Event Extraction
Xiao Liu, Heyan Huang, Ge Shi +1
We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequen…