16 citations · 59 across the 21 of their papers we have counts for
18 papers · 1 filter
LLM-jp-4 Technical Report
LLM-jp, :, Chaoran Liu +16
This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, and practical usability. LLM-…
Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models
Chengzhi Zhong, Fei Cheng, Qianying Liu +3
Large language models exhibit strong multilingual capabilities despite limited exposure to non-English data. Prior studies show that English-centric large language models map multi…
Assessing Agentic Large Language Models in Multilingual National Bias
Qianying Liu, Katrina Qiyao Wang, Fei Cheng +1
Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases ar…
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition
Zhengdong Yang, Qianying Liu, Sheng Li +2
We present a novel approach centered on the decoding stage of Automatic Speech Recognition (ASR) that enhances multilingual performance, especially for low-resource languages. It u…
Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?
Chengzhi Zhong, Fei Cheng, Qianying Liu +5
In this study, we investigate whether non-English-centric LLMs, despite their strong performance, `think' in their respective dominant language: more precisely, `think' refers to h…
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
Zhuoyuan Mao, Raj Dabre, Qianying Liu +3
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST). Recent efforts for ZST often utilize the Transformer architecture as the backbone,…
Comprehensive Solution Program Centric Pretraining for Table-and-Text Hybrid Numerical Reasoning
Qianying Liu, Dongsheng Yang, Wenjie Zhong +2
Numerical reasoning over table-and-text hybrid passages, such as financial reports, poses significant challenges and has numerous potential applications. Noise and irrelevant varia…
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
Zhen Wan, Fei Cheng, Zhuoyuan Mao +4
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.…
Textual Enhanced Contrastive Learning for Solving Math Word Problems
Yibin Shen, Qianying Liu, Zhuoyuan Mao +2
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies sh…
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Zhen Wan, Qianying Liu, Zhuoyuan Mao +3
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations…
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision
Qianying Liu, Wenyu Guan, Jianhao Shen +2
Previous studies have introduced a weakly-supervised paradigm for solving math word problems requiring only the answer value annotation. While these methods search for correct valu…
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems
Yibin Shen, Qianying Liu, Zhuoyuan Mao +3
To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions. However, the mainstream sequence-to-sequence approa…
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Zhen Wan, Fei Cheng, Qianying Liu +3
Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks. However, the existing methods ignore the intri…
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan +5
Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence gen…
Reverse Operation based Data Augmentation for Solving Math Word Problems
Qianying Liu, Wenyu Guan, Sujian Li +3
Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more hi…
LiveQA: A Question Answering Dataset over Sports Live
Qianying Liu, Sicong Jiang, Yizhong Wang +1
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human comme…
A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa, Frederic Bergeron, Junjie Chen +26
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhil…
CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning
Daojian Zeng, Ranran Haoran Zhang, Qianying Liu
Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is…