3 citations · 12 across the 27 of their papers we have counts for
14 papers · 1 filter
Align to the Pivot: Dual Alignment with Self-Feedback for Multilingual Math Reasoning
Chunxu Zhao, Xin Huang, Xue Han +3
Despite the impressive reasoning abilities demonstrated by large language models (LLMs), empirical evidence indicates that they are not language agnostic as expected, leading to pe…
Self-Correction Distillation for Structured Data Question Answering
Yushan Zhu, Wen Zhang, Long Jin +8
Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have…
JT-Safe: Intrinsically Enhancing the Safety and Trustworthiness of LLMs
Junlan Feng, Fanyu Meng, Chong Long +12
The hallucination and credibility concerns of large language models (LLMs) are global challenges that the industry is collectively addressing. Recently, a significant amount of adv…
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts
Qing Wang, Xue Han, Jiahui Wang +5
Despite LLMs' excellent code creation capabilities, multilingual code generation remains extremely challenging. To address this, we intent to improve the multi-programming-lingual…
JT-Math: A Multi-Stage Framework for Advanced Mathematical Reasoning in Large Language Models
Yifan Hao, Fangning Chao, Yaqian Hao +6
Mathematical reasoning is a cornerstone of artificial general intelligence and a primary benchmark for evaluating the capabilities of Large Language Models (LLMs). While state-of-t…
TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
Ce Li, Xiaofan Liu, Zhiyan Song +9
The majority of data in businesses and industries is stored in tables, databases, and data warehouses. Reasoning with table-structured data poses significant challenges for large l…
Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training
Zhijun Wang, Jiahuan Li, Hao Zhou +7
Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data. In this paper, we closely examine the rea…
Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From
Changjiang Gao, Hankun Lin, Xin Huang +5
Cross-lingual context retrieval (extracting contextual information in one language based on requests in another) is a fundamental aspect of cross-lingual alignment, but the perform…
Temporal Alignment of LLMs through Cycle Encoding for Long-Range Time Representations
Xue Han, Qian Hu, Yitong Wang +6
Large language models (LLMs) suffer from temporal misalignment issues especially across long span of time. The issue arises from knowing that LLMs are trained on large amounts of d…
SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks
Hongye Cao, Sijia Jing, Yanming Wang +14
With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on…
MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing
Hao Zhou, Zhijun Wang, Shujian Huang +6
Large Language Models (LLMs) are often English-centric due to the disproportionate distribution of languages in their pre-training data. Enhancing non-English language capabilities…
Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners
Shimao Zhang, Changjiang Gao, Wenhao Zhu +6
Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, mul…
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners
Peng Hu, Sizhe Liu, Changjiang Gao +5
Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less e…
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations
Jie Liu, Yixuan Liu, Xue Han +2
Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transform…