3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Siya Qi, Yudong Chen, Runcong Zhao +6
Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…
Evaluating LLMs' Assessment of Mixed-Context Hallucination Through the Lens of Summarization
Siya Qi, Rui Cao, Yulan He +1
With the rapid development of large language models (LLMs), LLM-as-a-judge has emerged as a widely adopted approach for text quality evaluation, including hallucination evaluation.…
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases
Rena Gao, Xuetong Wu, Tatsuki Kuribayashi +6
This study evaluates Large Language Models' (LLMs) ability to simulate non-native-like English use observed in human second language (L2) learners interfered with by their native f…
LLM-based Code-Switched Text Generation for Grammatical Error Correction
Tom Potter, Zheng Yuan
With the rise of globalisation, code-switching (CSW) has become a ubiquitous part of multilingual conversation, posing new challenges for natural language processing (NLP), especia…
A Survey of Automatic Hallucination Evaluation on Natural Language Generation
Siya Qi, Lin Gui, Yulan He +1
The rapid advancement of Large Language Models (LLMs) has brought a pressing challenge: how to reliably assess hallucinations to guarantee model trustworthiness. Although Automatic…