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20232026
most citedEvaluating Hallucinations in Chinese Large Language Models

8 citations · 8 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents

Shuai Bai, Jiayong Deng, Sicheng Fan +30

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both…

cs.CL2025

Thus Spake Long-Context Large Language Model

Xiaoran Liu, Ruixiao Li, Mianqiu Huang +11

Long context is an important topic in Natural Language Processing (NLP), running through the development of NLP architectures, and offers immense opportunities for Large Language M…

cs.CL2024

LongSafety: Enhance Safety for Long-Context LLMs

Mianqiu Huang, Xiaoran Liu, Shaojun Zhou +11

Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for th…

cs.CL2024

MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time

Mozhi Zhang, Pengyu Wang, Chenkun Tan +4

Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs mor…

cs.CL2024

Calibrating the Confidence of Large Language Models by Eliciting Fidelity

Mozhi Zhang, Mianqiu Huang, Rundong Shi +5

Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit o…

cs.CL2023★ 8 cited

Evaluating Hallucinations in Chinese Large Language Models

Qinyuan Cheng, Tianxiang Sun, Wenwei Zhang +8

In this paper, we establish a benchmark named HalluQA (Chinese Hallucination Question-Answering) to measure the hallucination phenomenon in Chinese large language models. HalluQA c…