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20232025
most citedBenchmarking LLMs via Uncertainty Quantification

9 citations · 12 across the 9 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024

TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models

Zelin Li, Kehai Chen, Lemao Liu +4

With the great advancements in large language models (LLMs), adversarial attacks against LLMs have recently attracted increasing attention. We found that pre-existing adversarial a…

cs.CL2024★ 2 cited

An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation

Cheng Yang, Guoping Huang, Mo Yu +6

Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neura…

cs.CL2024★ 1 cited

CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration

Xinming Hou, Mingming Yang, Wenxiang Jiao +3

Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In t…

cs.CL2024

On the Hallucination in Simultaneous Machine Translation

Meizhi Zhong, Kehai Chen, Zhengshan Xue +3

It is widely known that hallucination is a critical issue in Simultaneous Machine Translation (SiMT) due to the absence of source-side information. While many efforts have been mad…

cs.CL2024★ 9 cited

Benchmarking LLMs via Uncertainty Quantification

Fanghua Ye, Mingming Yang, Jianhui Pang +5

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evalu…