1 citations · 1 across the 6 of their papers we have counts for
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BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
Guosheng Dong, Da Pan, Yiding Sun +17
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several ins…
MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic
Yuyan Zhou, Liang Song, Bingning Wang +1
The advent of large language models (LLMs) like GPT-4 has catalyzed the exploration of multi-task learning (MTL), in which a single model demonstrates proficiency across diverse ta…
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
Jie Chen, Yupeng Zhang, Bingning Wang +3
Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic…
Full-ECE: A Metric For Token-level Calibration on Large Language Models
Han Liu, Yupeng Zhang, Bingning Wang +2
Deep Neural Networks (DNNs) excel in various domains but face challenges in providing accurate uncertainty estimates, which are crucial for high-stakes applications. Large Language…
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding
Bingning Wang, Feiyang Lv, Ting Yao +4
Visual question answering is an important task in both natural language and vision understanding. However, in most of the public visual question answering datasets such as VQA, CLE…