40 citations · 58 across the 19 of their papers we have counts for
19 papers
To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation
Jiuheng Lin, Chen Zhang, Yansong Feng
While reinforcement learning (RL) significantly enhances LLM reasoning, its efficacy is severely undermined by Pre-RL data overlap, where RL datasets overlap with pretraining or SF…
Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion
Chen Zhang, Jiuheng Lin, Zhiyuan Liao +1
Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although Proxy Tuning offers a logit…
An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
Yinhan Lu, Gaganpreet Jhajj, Chen Zhang +2
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent…
Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books
Chen Zhang, Jiuheng Lin, Xiao Liu +2
While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains…
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon
Chen Zhang, Zhiyuan Liao, Yansong Feng
Despite substantial research efforts evaluating how well large language models~(LLMs) handle global cultural diversity, the mechanisms behind their cultural knowledge acquisition,…
MiLiC-Eval: Benchmarking Multilingual LLMs for China's Minority Languages
Chen Zhang, Mingxu Tao, Zhiyuan Liao +1
Large language models (LLMs) excel in high-resource languages but struggle with low-resource languages (LRLs), particularly those spoken by minority communities in China, such as T…
Unlocking the Potential of Model Merging for Low-Resource Languages
Mingxu Tao, Chen Zhang, Quzhe Huang +4
Adapting large language models (LLMs) to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT). However, this CT-then-SFT approach s…
Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?
Yutong Hu, Quzhe Huang, Mingxu Tao +2
Recent studies have shown that Large Language Models (LLMs) have the potential to process extremely long text. Many works only evaluate LLMs' long-text processing ability on the la…
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
Quzhe Huang, Zhenwei An, Nan Zhuang +7
In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adju…
Teaching Large Language Models an Unseen Language on the Fly
Chen Zhang, Xiao Liu, Jiuheng Lin +1
Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available…
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
Haowei Du, Quzhe Huang, Chen Li +3
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge graph (KG), which requires multiple steps of reasoning. Existing retrieval-based app…
MC: Towards Transparent and Culturally-Aware NLP for Minority Languages in China
Chen Zhang, Mingxu Tao, Quzhe Huang +3
Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China. This limitation stems from the scarcit…
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
Chen Zhang, Jiuheng Lin, Xiao Liu +3
The multi-answer phenomenon, where a question may have multiple answers scattered in the document, can be well handled by humans but is challenging enough for machine reading compr…
The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code
Xiao Liu, Da Yin, Chen Zhang +2
Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human thinking. Although large language models (LLMs) succeed in many NLP tasks, it is still…
Lawyer LLaMA Technical Report
Quzhe Huang, Mingxu Tao, Chen Zhang +5
Large Language Models (LLMs), like LLaMA, have exhibited remarkable performance across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the m…
Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension
Chen Zhang, Yuxuan Lai, Yansong Feng +3
Although many large-scale knowledge bases (KBs) claim to contain multilingual information, their support for many non-English languages is often incomplete. This incompleteness giv…
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Haowei Du, Quzhe Huang, Chen Zhang +1
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing…
Extract, Integrate, Compete: Towards Verification Style Reading Comprehension
Chen Zhang, Yuxuan Lai, Yansong Feng +1
In this paper, we present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao. Different from existing efforts, the new datas…
Why Machine Reading Comprehension Models Learn Shortcuts?
Yuxuan Lai, Chen Zhang, Yansong Feng +2
Recent studies report that many machine reading comprehension (MRC) models can perform closely to or even better than humans on benchmark datasets. However, existing works indicate…