6 citations · 8 across the 27 of their papers we have counts for
8 papers · 1 filter
Benchmark^2: Systematic Evaluation of LLM Benchmarks
Qi Qian, Chengsong Huang, Jingwen Xu +13
The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose B…
Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning
Muling Wu, Qi Qian, Wenhao Liu +12
Large Language Models (LLMs) have achieved remarkable performance across various reasoning tasks, yet post-training is constrained by inefficient sample utilization and inflexible…
Improving Continual Pre-training Through Seamless Data Packing
Ruicheng Yin, Xuan Gao, Changze Lv +3
Continual pre-training has demonstrated significant potential in enhancing model performance, particularly in domain-specific scenarios. The most common approach for packing data b…
Chain-of-Model Learning for Language Model
Kaitao Song, Xiaohua Wang, Xu Tan +14
In this paper, we propose a novel learning paradigm, termed Chain-of-Model (CoM), which incorporates the causal relationship into the hidden states of each layer as a chain style,…
Enhancing the Capability and Robustness of Large Language Models through Reinforcement Learning-Driven Query Refinement
Xiaohua Wang, Zisu Huang, Feiran Zhang +5
The capacity of large language models (LLMs) to generate honest, harmless, and helpful responses heavily relies on the quality of user prompts. However, these prompts often tend to…
Searching for Best Practices in Retrieval-Augmented Generation
Xiaohua Wang, Zhenghua Wang, Xuan Gao +11
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, partic…
Aligning Large Language Models with Human Preferences through Representation Engineering
Wenhao Liu, Xiaohua Wang, Muling Wu +7
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness…
UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulation
Tianlong Li, Wenhao Liu, Muling Wu +6
Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs) holds significant potential in enhanc…