128 citations · 724 across the 72 of their papers we have counts for
7 papers · 1 filter
Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data
Shuai Zhao, Yunqiu Xu, Linchao Zhu +1
Large language models~(LLMs) are expected to be helpful, harmless, and honest. In different alignment scenarios, such as safety, confidence, and general preference alignment, binar…
FragRel: Exploiting Fragment-level Relations in the External Memory of Large Language Models
Xihang Yue, Linchao Zhu, Yi Yang
To process contexts with unlimited length using Large Language Models (LLMs), recent studies explore hierarchically managing the long text. Only several text fragments are taken fr…
Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training
Shuai Zhao, Linchao Zhu, Ruijie Quan +1
A primary concern regarding training large language models (LLMs) is whether they abuse copyrighted online text. With the increasing training data scale and the prevalence of LLMs…
AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents
Yuanzhi Liang, Linchao Zhu, Yi Yang
Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-cha…
Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models
Yuanzhi Liang, Linchao Zhu, Yi Yang
Recent advancements in natural language and Large Language Models (LLMs) have enabled AI agents to simulate human-like interactions within virtual worlds. However, these interactio…
Whitening-based Contrastive Learning of Sentence Embeddings
Wenjie Zhuo, Yifan Sun, Xiaohan Wang +2
This paper presents a whitening-based contrastive learning method for sentence embedding learning (WhitenedCSE), which combines contrastive learning with a novel shuffled group whi…
SemGloVe: Semantic Co-occurrences for GloVe from BERT
Leilei Gan, Zhiyang Teng, Yue Zhang +3
GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local conte…