7 papers · 1 filter
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Yue Zhang, Yafu Li, Leyang Cui +13
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit h…
Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks
Qintong Li, Leyang Cui, Lingpeng Kong +1
Previous work adopts large language models (LLMs) as evaluators to evaluate natural language process (NLP) tasks. However, certain shortcomings, e.g., fairness, scope, and accuracy…
Gated Slot Attention for Efficient Linear-Time Sequence Modeling
Yu Zhang, Songlin Yang, Ruijie Zhu +9
Linear attention Transformers and their gated variants, celebrated for enabling parallel training and efficient recurrent inference, still fall short in recall-intensive tasks comp…
Rethinking Targeted Adversarial Attacks For Neural Machine Translation
Junjie Wu, Lemao Liu, Wei Bi +1
Targeted adversarial attacks are widely used to evaluate the robustness of neural machine translation systems. Unfortunately, this paper first identifies a critical issue in the ex…
CORM: Cache Optimization with Recent Message for Large Language Model Inference
Jincheng Dai, Zhuowei Huang, Haiyun Jiang +4
Large Language Models (LLMs), despite their remarkable performance across a wide range of tasks, necessitate substantial GPU memory and consume significant computational resources.…
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
Yafu Li, Zhilin Wang, Leyang Cui +3
AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-para…