Text generated by OPUS-MT and T5 models with single-bit errors in the parameters

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}}