Leia-Swallow-7B

LEIA is a training technique for autoregressive LLMs that effectively improves their performance in languages other than English by enhancing cross-lingual knowledge transfer from English to a target language. This model is constructed by applying LEIA to Swallow, a Japanese-English bilingual LLM based on LLaMA 2. The model achieves enhanced performance on six Japanese question-answering benchmarks, as reported below.

Please refer to our paper or blog post (in Japanese) for further technical details.

• LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation (arxiv.org)
• LEIA: 言語間転移学習でLLMを賢くする新しい方法 (zenn.dev)

Model List

• Leia-Swallow-7b
• Leia-Swallow-13b

Empirical Results

The model is assessed using the following six question answering benchmarks:

• X-CODAH
• X-CSQA
• JCommonsenseQA
• NIILC
• JEMHopQA
• JAQKET v2

Model	X-CODAH	X-CSQA	JCommonsenseQA	NIILC	JEMHopQA	JAQKET v2
Swallow	42.0	41.0	80.3	59.5	50.8	86.2
LEIA	42.7	42.4	80.6	60.3	54.7	86.5

For further details of this experiment, please refer to our paper.

Contributors

• Ikuya Yamada (Studio Ousia, RIKEN)
• Ryokan Ri (LY Corporation, SB Intuitions)