In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
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Question: The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best. In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm. 
 Question: what are the advantages of the proposed model?

Answer: he proposed model outperforms all the baselines, being the svi version the one that performs best. the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.


Question: This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.

Results and Discussion 
 Question: What are the six target languages?

Answer: Answer with content missing: (3 Experimental Setup) We experiment with six target languages: French (FR), Brazilian Portuguese (PT), Italian (IT), Polish (PL), Croatian (HR), and Finnish (FI).


Question: We propose the following challenge to the community: We must develop formal definition and evaluation for faithfulness that allows us the freedom to say when a method is sufficiently faithful to be useful in practice.

We note two possible approaches to this end:

Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Perhaps some models or tasks allow sufficiently faithful interpretation, even if the same is not true for others.

For example, the method may not be faithful for some question-answering task, but faithful for movie review sentiment, perhaps based on various syntactic and semantic attributes of those tasks.

Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves. If we are able to say with some degree of confidence whether a specific decision's explanation is faithful to the model, even if the interpretation method is not considered universally faithful, it can be used with respect to those specific areas or instances only. 
 Question: What faithfulness criteria does they propose?

Answer:
Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves.