9 citations · 21 across the 22 of their papers we have counts for
5 papers · 1 filter
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
Baohao Liao, Christof Monz
Parameter-efficient finetuning (PEFT) methods effectively adapt large language models (LLMs) to diverse downstream tasks, reducing storage and GPU memory demands. Despite these adv…
IKUN for WMT24 General MT Task: LLMs Are here for Multilingual Machine Translation
Baohao Liao, Christian Herold, Shahram Khadivi +1
This paper introduces two multilingual systems, IKUN and IKUN-C, developed for the general machine translation task in WMT24. IKUN and IKUN-C represent an open system and a constra…
The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models
Xinyi Chen, Baohao Liao, Jirui Qi +4
Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant challenges: (i) limited coherence between mult…
Is It a Free Lunch for Removing Outliers during Pretraining?
Baohao Liao, Christof Monz
With the growing size of large language models, the role of quantization becomes increasingly significant. However, outliers present in weights or activations notably influence the…
ApiQ: Finetuning of 2-Bit Quantized Large Language Model
Baohao Liao, Christian Herold, Shahram Khadivi +1
Memory-efficient finetuning of large language models (LLMs) has recently attracted huge attention with the increasing size of LLMs, primarily due to the constraints posed by GPU me…