4 citations · 12 across the 16 of their papers we have counts for
16 papers
SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models
Jing Yu, Yuqi Tang, Kehua Feng +8
Large Language Models (LLMs) have shown impressive capabilities in contextual understanding and reasoning. However, evaluating their performance across diverse scientific domains r…
LookAhead Tuning: Safer Language Models via Partial Answer Previews
Kangwei Liu, Mengru Wang, Yujie Luo +7
Fine-tuning enables large language models (LLMs) to adapt to specific domains, but often compromises their previously established safety alignment. To mitigate the degradation of m…
Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation
Zhouyu Jiang, Mengshu Sun, Zhiqiang Zhang +1
Retrieval-Augmented Generation (RAG) effectively reduces hallucinations in Large Language Models (LLMs) but can still produce inconsistent or unsupported content. Although LLM-as-a…
K-ON: Stacking Knowledge On the Head Layer of Large Language Model
Lingbing Guo, Yichi Zhang, Zhongpu Bo +5
Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next tok…
MAQInstruct: Instruction-based Unified Event Relation Extraction
Jun Xu, Mengshu Sun, Zhiqiang Zhang +1
Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching.…
Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis
Lin Yuan, Jun Xu, Honghao Gui +4
High-quality, large-scale instructions are crucial for aligning large language models (LLMs), however, there is a severe shortage of instruction in the field of natural language un…