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20182023
most citedAligning Large Language Models with Human: A Survey

54 citations · 83 across the 10 of their papers we have counts for

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13 papers · 1 filter

cs.CL20241 cited

Let's Negotiate! A Survey of Negotiation Dialogue Systems

Haolan Zhan, Yufei Wang, Tao Feng +7

Negotiation is a crucial ability in human communication. Recently, there has been a resurgent research interest in negotiation dialogue systems, whose goal is to create intelligent…

cs.CL20241 cited

MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models

Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang +6

Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications. However, existing benchmarks predominantly fo…

cs.CL20241 cited

YODA: Teacher-Student Progressive Learning for Language Models

Jianqiao Lu, Wanjun Zhong, Yufei Wang +10

Although large language models (LLMs) have demonstrated adeptness in a range of tasks, they still lag behind human learning efficiency. This disparity is often linked to the inhere…

cs.CL2024

Importance-Aware Data Augmentation for Document-Level Neural Machine Translation

Minghao Wu, Yufei Wang, George Foster +2

Document-level neural machine translation (DocNMT) aims to generate translations that are both coherent and cohesive, in contrast to its sentence-level counterpart. However, due to…

cs.CL2023

Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning

Xindi Wang, Yufei Wang, Can Xu +6

Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explici…

cs.CL202354 cited

Aligning Large Language Models with Human: A Survey

Yufei Wang, Wanjun Zhong, Liangyou Li +6

Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their nota…