Yuanxing Zhang
2024
E2-LLM: Efficient and Extreme Length Extension of Large Language Models
Jiaheng Liu
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ZhiqiBai ZhiqiBai
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Yuanxing Zhang
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Chenchen Zhang
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YuangZh YuangZh
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Ge Zhang
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JiakaiWang JiakaiWang
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Haoran Que
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Yukang Chen
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Wenbo Su
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Tiezheng Ge
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Jie Fu
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Wenhu Chen
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Bo Zheng
Findings of the Association for Computational Linguistics ACL 2024
Training Large Language Models (LLMs) to process extensive context lengths incurs prohibitive computational costs. Prevailing techniques for extending context capabilities in LLMs typically require not only additional training procedures but also access to datasets with long context (e.g., sequences of 32K tokens), presupposing substantial GPU expenditures. To address the aforementioned issues, we introduce a novel solution named Efficient and Extreme length extension for Large Language Models (E2-LLM). E2-LLM entails a singular training process over considerably short sequences (e.g., 4K tokens), which greatly mitigates the cost of continual-pretraining or fine-tuning. Within the training phase, we incorporate a dual augmentation strategy with Rotary Position Embeddings (RoPE) that adjusts the scale and position indices across distinct training samples. E 2 -LLM is meticulously designed to enhance the model’s robustness to diverse relative positions. The experimental results on multiple benchmark datasets demonstrate the superior performance of E 2 -LLM on demanding tasks of processing long contexts.
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
Yanan Wu
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Jie Liu
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Xingyuan Bu
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Jiaheng Liu
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Zhanhui Zhou
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Yuanxing Zhang
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Chenchen Zhang
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ZhiqiBai ZhiqiBai
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Haibin Chen
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Tiezheng Ge
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Wanli Ouyang
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Wenbo Su
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Bo Zheng
Findings of the Association for Computational Linguistics ACL 2024
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unlike traditional benchmarks that evaluate general mathematical reasoning with an average accuracy, ConceptMath systemically organizes math problems under a hierarchy of math concepts, so that mathematical reasoning can be evaluated at different granularity with concept-wise accuracies. Based on our ConcepthMath, we then evaluate a broad range of LLMs, and we observe existing LLMs, though achieving high average accuracies on traditional benchmarks, exhibit significant performance variations across different math concepts and may even fail catastrophically on the most basic ones. Besides, we also introduce an efficient fine-tuning strategy to enhance the weaknesses of existing LLMs. Finally, we hope ConceptMath could guide the developers to understand the fine-grained mathematical abilities of their models and facilitate the growth of foundation models. Code is available at https://github.com/conceptmath/conceptmath.
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Co-authors
- Jiaheng Liu 2
- ZhiqiBai ZhiqiBai 2
- Chenchen Zhang 2
- Wenbo Su 2
- Tiezheng Ge 2
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