3 citations · 4 across the 5 of their papers we have counts for
5 papers
Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging
Mengxian Lyu, Cheng Peng, Ziyi Chen +3
Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a signi…
LLMs Struggle with NLI for Perfect Aspect: A Cross-Linguistic Study in Chinese and Japanese
Jie Lu, Du Jin, Hitomi Yanaka
Unlike English, which uses distinct forms (e.g., had, has, will have) to mark the perfect aspect across tenses, Chinese and Japanese lack separate grammatical forms for tense withi…
Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
Hitomi Yanaka, Xinqi He, Jie Lu +6
An increasing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a…
JBBQ: Japanese Bias Benchmark for Analyzing Social Biases in Large Language Models
Hitomi Yanaka, Namgi Han, Ryoma Kumon +5
With the development of large language models (LLMs), social biases in these LLMs have become a pressing issue. Although there are various benchmarks for social biases across langu…
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Luchang Li, Sheng Qian, Jie Lu +3
The Large Language Model (LLM) is widely employed for tasks such as intelligent assistants, text summarization, translation, and multi-modality on mobile phones. However, the curre…