Text generated by OPUS-MT and T5 models with single-bit errors in the parameters
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https://zenodo.org/doi/10.5281/zenodo.15512473
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Description
The dataset contains text generated using T5 and OPUS-MT model with and with single-bit errors in the parameters of the LLM. The T5 LLM used the CNN Daily Mail dataset for summarization and OPUS-MT used the IWSLT2017 dataset for Chinese-to-English translation.
Folders:
t5_fp32: T5 model with a quantified version of FP32
t5_fp16: T5 model with a quantified version of FP16
opus_fp32: OPUS-MT model with a quantified version of FP32
opus_fp16: OPUS-MT model with a quantified version of FP16
Files:
{cnn/iwslt2017}_input_text.txt: Input text, that is, text to summarize (cnn and T5) or Chinese text to translate (iwslt2017 and OPUS-MT). For each dataset in total there are number_input_texts.
{cnn/iwslt2017}_output_reference.txt: Example of result expected for CNN (T5) and IWSLT2017 (OPUS-MT). For each dataset in total there are number_input_texts.
{cnn/iwslt2017}_output_predict_fault_free: Example of predictions without single-bit errors. For each dataset in total there are number_input_texts.
{cnn/iwslt2017}_output_predict_single_fi_bit_100times: Example of predictions with 100 different single-bit error. In each dataset in total there are 100*number input texts.
Paper
Paper: Concurrent Linguistic Error Detection (CLED) for Large Language Models
Cite:
@misc{zhu2024concurrent, title={Concurrent Linguistic Error Detection (CLED) for Large Language Models}, author={Jinhua Zhu and Javier Conde and Zhen Gao and Pedro Reviriego and Shanshan Liu and Fabrizio Lombardi}, year={2024}, eprint={2403.16393}, archivePrefix={arXiv}, primaryClass={cs.AI}}
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Zenodo创建时间：
2025-05-25
