activity
20232025
most citedBenchmarking LLMs via Uncertainty Quantification

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

collaborators

9 papers

cs.RO2025

World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild

Yupeng Zheng, Jichao Peng, Weize Li +22

We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale moti…

cs.LG2025

ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism

Jia Liu, ChangYi He, YingQiao Lin +3

Recent advancements in Large Language Models have yielded significant improvements in complex reasoning tasks such as mathematics and programming. However, these models remain heav…

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…

cs.CL2023

Context Consistency between Training and Testing in Simultaneous Machine Translation

Meizhi Zhong, Lemao Liu, Kehai Chen +2

Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing the source-side context. However, there is a counterintuitive phe…

cs.CL2023

Rethinking Word-Level Auto-Completion in Computer-Aided Translation

Xingyu Chen, Lemao Liu, Guoping Huang +4

Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation. It aims at providing word-level auto-completion suggestions for human translators. While pr…