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question:Mask-filling: Given a sequence of tables T and a question Q with [MASK], what is the word at [MASK]. Q： 5.23 for Faster R-CNN early fusion multi-tasking, down to 4.83 with just COCO Captions).the COCO-only setup, we see a decrease in perplexity from 5.21 to 4.83; with [MASK] WSL Spatial image features, we see perplexity differen T:<C>Dataset Average, COCO & pushshift . io Reddit training data,Model ResNeXt WSL Late,12.45,Model ResNeXt WSL Spatial Early,10.10,Model ResNeXt WSL Spatial Late,10.22,Model ResNeXt WSL Spatial Early,10.00, Faster R - CNN Late,<max>9.21,Model ResNeXt WSL Spatial Early,9.13<bold>,Model ResNeXt WSL Late,12.67,Model ResNeXt WSL Spatial Early,13.76,Model ResNeXt WSL Spatial Late,11.51,Model ResNeXt WSL Spatial Early,12.48, Faster R - CNN Late,11.55,Model ResNeXt WSL Spatial Early,11.82,<C>Dataset COCO , COCO & pushshift . io Reddit training data,Model ResNeXt WSL Late,11.11,Model ResNeXt WSL Spatial Early,6.69,Model ResNeXt WSL Spatial Late,<max>7.43,Model ResNeXt WSL Spatial Early,6.53, Faster R - CNN Late,5.26,Model ResNeXt WSL Spatial Early,5.23,Model ResNeXt WSL Late,5.82,Model ResNeXt WSL Spatial Early,6.21,Model ResNeXt WSL Spatial Late,6.51,Model ResNeXt WSL Spatial Early,6.19, Faster R - CNN Late,5.21,Model ResNeXt WSL Spatial Early,4.83<bold>,<C> pushshift . io Reddit , COCO & pushshift . io Reddit training data,Model ResNeXt WSL Late,13.80,Model ResNeXt WSL Spatial Early,13.50,Model ResNeXt WSL Spatial Late,13.00,Model ResNeXt WSL Spatial Early,13.46, Faster R - CNN Late,13.17,Model ResNeXt WSL Spatial Early,13.15,Model ResNeXt WSL Late,19.52,Model ResNeXt WSL Spatial Early,<max>21.30,Model ResNeXt WSL Spatial Late,16.50,Model ResNeXt WSL Spatial Early,18.77, Faster R - CNN Late,17.88,Model ResNeXt WSL Spatial Early,18.81,Table 10: Model performance, measured via perplexity on validation data, on domain-adaptive pre-training datasets, comparing various image features and image fusion techniques. The top three rows involve multi-task training on COCO Captions and pushshift.io Reddit, while the bottom three rows involve single task training on COCO Captions only. We note that early fusion with Faster R-CNN features yields the best performance on COCO Captions.

answer:Late

question:Mask-filling: Given a sequence of tables T and a question Q with [MASK], what is the word at [MASK]. Q： Although our proposed baseline RAN (k = 16) achieves a slightly higher accuracy (just 1.2% difference) with BERT under [MASK] attack, its accuracy rapidly drops to 27% under the more sophisticated attack GA, while DNE still yield T:<C>Model PWWS, ORIG,42.6, ADV,68.2,Model RAN,<max>72.7<bold>,Model DNE,71.5,<C>Model PWWS, ORIG,23.1, ADV,71.7,Model IBP,72.8,Model RAN,67.2,Model DNE,<max>77.4<bold>,<C>Model GA - LM, ORIG,56.7, ADV,79.0,Model RAN,42.7,Model DNE,<max>80.1<bold>,<C>Model GA, ORIG,<max>8.1, ADV,65.2,Model IBP,70.5,Model RAN,30.6,Model DNE,74.6<bold>,<C>Model GA, ORIG,<max>8.26, ADV,59.5,Model IBP,71.0,Model RAN,27.8,Model DNE,71.5,<C>Model GA - LM, ORIG,41.6, ADV,71.0,Model IBP,75.0,Model RAN,44.4,Model DNE,<max>75.3<bold>,<C>Model PWWS, ORIG,24.4, ADV,67.9,Model IBP,74.9,Model RAN,65.7,Model DNE,<max>76.3<bold>,<C>Model GA - LM, ORIG,40.8, ADV,73.8,Model IBP,73.7,Model RAN,51.1,Model DNE,<max>76.7<bold>,<C> CLN, ORIG,81.2, ADV,<max>81.9<bold>,Model IBP,77.3,Model RAN,80.3,Model DNE,80.2,<C>Model GA, ORIG,19.9, ADV,58.2,Model RAN,27.0,Model DNE,<max>62.7<bold>,<C> CLN, ORIG,<max>80.9<bold>, ADV,80.4,Model IBP,79.3,Model RAN,79.0,Model DNE,79.8,<C> CLN, ORIG,<max>90.5<bold>, ADV,89.4,Model RAN,89.9,Model DNE,90.1,Table 3: Natural language inference on SNLI dataset.

answer:The word at [MASK] is "adversarial".

question:Mask-filling: Given a sequence of tables T and a question Q with [MASK], what is the word at [MASK]. Q： For example, Supported claim "Washing hands prevents the [MASK] of diseases," has a perplexity value of 96.74, whereas the Unsupported claim "All dogs speak English fluently," has a much higher perplexity value of 328.23. We be T:<C> Perplexity, 5G network can spread diseases .,<max>826.70, All dogs speak English fluently .,328.23, Washing hands helps the spread of diseases .,201.10,<C> Perplexity, 5G network can spread diseases . NAME_1 is one of the most famous singers in the world .,23.03, All dogs speak English fluently . Chicago is one of the cities in the United States .,43.92, Washing hands helps the spread of diseases . Washing hands prevents the spread of diseases .,<max>96.74,Table 1: Relations between veracity of claim and perplexity. Unsupported claims have higher perplexity compared to Supported claims. Note that the perplexity score listed here is using GPT2-base on each of the claims.

answer:Given the sequence of tables T and question Q with [MASK], we can fill in the word at [MASK] using the information provided in Table 1. Q: What is the word that should fill the [MASK] in the following claim: "Washing hands prevents the spread of diseases," has a perplexity value of 96.74, whereas the Unsupported claim "All dogs speak English fluently," has a much higher perplexity value of 328.23. A: In the given claim, the word that should fill the [MASK] is "spread." Explanation: The given claim is "Washing hands prevents the spread of diseases." We are asked to find the word that should fill the [MASK] based on the information in Table 1. According to the table, Supported claims have lower perplexity values compared to Unsupported claims. In this case, the claim "Washing hands prevents the spread of diseases" is a Supported claim. Since we are looking for the word that should fill the [MASK] in a Supported claim, we can refer to the example provided in the table: "Washing hands helps the spread of diseases." In this example, the word that should fill the [MASK] is "spread." Therefore, in the given claim, the word that should fill the [MASK] is "spread."

question:Mask-filling: Given a sequence of tables T and a question Q with [MASK], what is the word at [MASK]. Q： For example, the binarised performance of the 4-class and 55-class models is slightly higher than the [MASK] model on BEA-dev (66.10 and 65.81 vs. 65.54). This may suggest that all systems are capable of detecting roughly the s T:<C> BEA - dev binarised, F,Model binary,65.54,Model 4 - class,<max>66.10<bold>, 25 - class,63.08, 55 - class,65.81,<C>Metric FCE - test macro, F,Model binary,<max>83.54,Model 4 - class,70.95, 25 - class,54.59, 55 - class,34.88,<C> BEA - dev macro, F,Model binary,<max>80.39,Model 4 - class,67.07, 25 - class,47.28, 55 - class,32.99,<C>Metric FCE - test binarised, F,Model binary,72.93,Model 4 - class,72.57, 25 - class,72.08, 55 - class,<max>73.85<bold>,<C> BEA - dev,Model binary,<max>0.5<bold>,<C>Metric FCE - test, 25 - class F,<max>0.5<bold>,Table 3: Binary and multi-class error detection per-formance of the ELECTRA GED model trained on the FCE. The highest binarised F 0.5 scores are in bold.

answer:Based on the context provided and the table, the word at [MASK] should be "binary". The sentence is comparing the performance of the 4-class and 55-class models to another model on BEA-dev, and the table shows that the other model is the "binary" model. So, the completed sentence would be: "For example, the binarised performance of the 4-class and 55-class models is slightly higher than the binary model on BEA-dev (66.10 and 65.81 vs. 65.54)."