23 citations · 69 across the 18 of their papers we have counts for
4 papers · 2 filters
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Renxi Wang, Haonan Li, Minghao Wu +4
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datas…
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
Neha Sengupta, Sunil Kumar Sahu, Bokang Jia +29
We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-…
Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
Yuxia Wang, Haonan Li, Xudong Han +2
With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging. This requires developers to be able to identify risks through t…
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP
Xudong Han, Timothy Baldwin, Trevor Cohn
Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. However current progress is hampered by a plurality of definitions…