15 citations · 28 across the 12 of their papers we have counts for
18 papers · 1 filter
Neuron-based Personality Trait Induction in Large Language Models
Jia Deng, Tianyi Tang, Yanbin Yin +3
Large language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting related applications (e.g., role-p…
LLMBox: A Comprehensive Library for Large Language Models
Tianyi Tang, Yiwen Hu, Bingqian Li +22
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. T…
Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
Yushuo Chen, Tianyi Tang, Erge Xiang +5
In real world, large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide…
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models
Tianyi Tang, Wenyang Luo, Haoyang Huang +5
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. It remains a challenging…
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
Geyang Guo, Ranchi Zhao, Tianyi Tang +2
Alignment with human preference is a desired property of large language models (LLMs). Currently, the main alignment approach is based on reinforcement learning from human feedback…
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
Zican Dong, Tianyi Tang, Junyi Li +2
Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enh…