9 citations · 30 across the 14 of their papers we have counts for
5 papers · 1 filter
Alleviating Hallucinations of Large Language Models through Induced Hallucinations
Yue Zhang, Leyang Cui, Wei Bi +1
Despite their impressive capabilities, large language models (LLMs) have been observed to generate responses that include inaccurate or fabricated information, a phenomenon commonl…
Reasons to Reject? Aligning Language Models with Judgments
Weiwen Xu, Deng Cai, Zhisong Zhang +2
As humans, we consistently interact with our peers and receive feedback in the form of natural language. This language feedback allows us to maintain appropriate behavior, and rect…
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…
RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation
Yue Zhang, Leyang Cui, Enbo Zhao +2
Grammatical Error Correction (GEC) systems play a vital role in assisting people with their daily writing tasks. However, users may sometimes come across a GEC system that initiall…
MAGE: Machine-generated Text Detection in the Wild
Yafu Li, Qintong Li, Leyang Cui +6
Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection to mitigate risks like the spread of fake new…