Neural sequence models have achieved impressive breakthroughs in a wide range of applications, including free-form question answering , machine translation (Vaswani et al., 2017; Wu et al., 2016) and text summarization (Rothe et al., 2020) . Although these models can generate fluent sentences that are even sometimes preferred to human-written content (Läubli et al., 2018; Brown et al., 2020) , recent work has also shown that they lack global logical consistency (Marcus & Davis, 2020) , sometimes degenerate to dull and repetitive outputs (Welleck et al., 2019) and can often hallucinate content that is not entailed by the input (Maynez et al., 2020) . In this paper, we focus on the faithfulness of machine outputs in conditional sequence generation tasks, aiming to automatically identify and quantify content in the output that is not faithful to the input text.
This risk of generating unfaithful content impedes the safe deployment of neural sequence generation models. The first step to building models that do not suffer from these failures is the assessment and identification of such hallucinated outputs. Prior work has shown that standard metrics used for sequence evaluation (Maynez et al., 2020; Wang & Sennrich, 2020) do not correlate well with the faithfulness of model outputs, and very recent efforts (Maynez et al., 2020; Durmus et al., 2020; Wang et al., 2020a) have started to develop automatic metrics to measure the faithfulness of output sequences. These methods use an external semantic model, e.g. for question-answering or textual entailment, to score faithfulness. However, these scores do not directly measure the number of hallucinated tokens and only correlate weakly with human judgements due to compounded errors. We propose a new task for faithfulness assessment -hallucination detection at the token level, which aims to predict if each token in the machine output is a hallucinated or faithful to the source input. This task does not use the reference output to assess faithfulness, which offers us the ability to apply it in the online generation scenario where references are not available. We measure hallucination for two conditional sequence generation tasks -abstractive summarization and machine transla- Figure 1 : A toy example of token-level hallucination detection from machine translation. The words in grey blocks is an example of machine translation output and the labels above them indicate if each word is faithful (0) to the source input or a hallucinated one (1).
tion (MT) . For the former, we produce a benchmark dataset from the recent released annotations in (Maynez et al., 2020) . For MT, we carefully design the human assessment guideline and create high-quality annotations. We will also release our human annotated data for future research.
To learn token-level hallucination prediction for general conditional sequence generations tasks, we propose a novel method that creates synthetic "hallucinated" dataset with pseudo labels and finetunes a pretrained language model (Liu et al., 2019; Conneau et al., 2020) on it. Without any human annotated supervised training data, we achieve an average F1 of around 0.6 across all the benchmark datasets, setting initial performance levels for this new task. We have also computed sentence-level aggregated predictions and achieve significantly higher correlations with human scores than previous methods. Finally, we use our new data to study the effect of pretraining on MT hallucination and show it can actually produce more faithful translations, confirming recent findings in abstractive summarization (Maynez et al., 2020) .
Commonly adopted metrics for sequence generations evaluations such as BLEU scores (Papineni et al., 2002; Post, 2018) , ROUGE (Lin & Hovy, 2004) and recently proposed quality estimation metrics including BLEURT (Sellam et al., 2020) and BERTScores have been shown that they correlate poorly with faithfulness (Maynez et al., 2020; Wang & Sennrich, 2020) . They also require reference text, limiting their applicability to detecting halluciations in a deployed system at run-time.
Recently, several efforts have been made on automatic evaluation of faithfulness and truthfulness in abstract text summarization (Wang et al., 2020a; Maynez et al., 2020; Durmus et al., 2020; Falke et al., 2019) by leveraging existing semantic inference systems: (1) question answering (QA) system-based methods (Wang et al., 2020a; Durmus et al., 2020) first employ a question generation system to generate natural questions based on the machine generated summary, then questions are answered with a QA system using the source article and the summary respectively. The final faithfulness score of the summary is given by comparing the corresponding answers.
(2) For the textual entailment-based methods, a natural language inference (NLI) system is trained to predict whether the source article entails the summary or not. Then the entailment scores are used as the proxy to assess the faithfulness of the summary. Nonetheless, these methods tailored for abstract text summarization are shown to have a weak correlation with human evaluation of faithfulness.
Similar to the spirit of our proposed task, word-level quality estimation (Fonseca et al., 2019) in the machine translation community predicts if tokens are correctly translated based on human postediting. However, they do not distinguish errors in terms of fluency and adequacy (Specia et al., 2011) . In contrast to estimating the amount of human post-editing work required to fix errors, we specifically identify hallucinations corresponding to only hallucination (not fluency) errors.
Our token-level hallucination detection task builds on the definition of hallucination introduced by Maynez et al. (2020) . For a source sequence S and its model generation G from a neural conditional generation model, we define any span w i , · · · , w i+j (j >= 0) in G as being hallucinated if it cannot be entailed by the source input S. More specifically, we consider two types of hallucination:
Mike goes to the bookstore on Thursday.
<MASK> goes to the bookstore <MASK>. Figure 2 : Generation of synthetic hallucinated data set with hallucination labels. The bottom block generates a hallucinated version of T by feeding the noised sentence to the pretrained encoderdecoder model BART. The top block assigns hallucination labels to each token by computing the edit-distance between T and T . Labels of 1 refer to hallucinated words and vice versa.
Content Insertion: a span w i , · · · , w i+j in G consists of additional content that is not supported by S, i.e. its paraphrase or other equivalent form cannot be inferred from S. In Fig. 1 , the word "happily" in the machine translation belongs to this case. This is also referred as "extrinsic hallucinations" in Maynez et al. (2020) .
Incorrect Substitution: a span of word(s) is misrepresented information based on the S. In Fig. 1 , "Jerry" in the machine translation is a hallucinated word and should be replaced by "Mike". This type of hallucination is similar to the concept of "intrinsic hallucination" in Maynez et al. (2020) . Note that there are cases where certain words (e.g. "This is not a great book." becomes "This is a great book.") are dropped in G and hence the meaning of S is changed, and we consider any spans in G that misrepresent S as hallucinated contents (e.g. the entire sentence of "This is a great book.").
We aim to identify all the span(s) satisfying the above conditions in the machine generation G. 2 Such fine-grained predictions of hallucination can not only provide the faithfulness level of the entire sentence by aggregating token-level predictions but also offer benefits for more general use cases by labeling specific hallucinated tokens (e.g. interpretability of generations).
We propose a general-purpose method for token-level hallucination detection for conditional sequence generation tasks. Given the source input S, we first formulate the task of token-level hallucination detection as a sequence labeling problem where a binary label is predicted at each position G t of the machine generation G. One straightforward way of learning such task is to train a model with supervised data in the form of ((S, G), L G ) where L G are the labels at every positions of G that indicate if each word is a hallucinated one or not. However, because such labeled training data is not readily available, we also propose a approach to automatically create synthetic training data.
We use bi-text from the training data to create synthetic examples by automatically inserting new, hallucinated tokens. More speficially, we take target sequence T and create a hallucinated version of it denoted T with associated hallucination labels for each token in T . Then we can train a supervised model on this synthetic labeled data set of ((S, T ), L T ). The key challenge is that T should be a fluent sentence that does not differ too much from T .
Generation of hallucinated sentences To control this synthetic hallucination process, we build on recent work on large scale denoising autoencoders. These models learn to map a corrupted sentence back to the original text it was derived from, including for example learning to reconstruct missing words that have been arbitrarily masked out. We use BART by first inserting noises and then reconstructing parts of the target sentence, without providing it any access to the source sentence, thereby encouraging it to insert new content as needed to ensure fluency. As shown in Fig. 2 , we first apply noising functions to the target sentence T in the bi-text and then use a pretrained BART to generate T conditioned on the noised T with beam search.
Label assignments After obtaining the hallucinated sentence T with BART, we need to assign appropriate labels to each token in T to mark which words are hallucinated. We compute the edit distance between T and T , and back-trace the deletion and substitution operations with dynamic programming. All the positions in T involving these two operations are labeled as hallucinations and everything else is considered faithful to T .
Mike goes to the bookstore on Thursday.
happily. T' T 1 0 0 0 0 1 1 1 Figure 3 : An example of label assignment. Fig. 3 shows an example of label assignment with edit distance, where words in red are replaced and the word in blue is deleted to convert T to T . Assigning labels with edit-distance can not always guarantee the correct labels, however, we find that this simple approach provides sufficiently high quality training data for effective hallucination detection in practice.
Hallucination prediction loss We follow the common practice in natural language understanding (NLU) tasks and finetune a pretrained language model (LM) on our synthetic data. We finetune a cross-lingual LM (Conneau et al., 2020) for machine translation and a monolingual model (Liu et al., 2019) for summarization. In both cases, we concatenate the input source, true target and hallucinated target denoted (S, T , T ) as a single input sequence to the pretrained LM. Then we minimize the standard classification loss L pred over the pseudo hallucination labels L T on top of the final hidden vectors of each token in T as shown in Fig. 4 .
Although using only the source text and hallucinated target (S, T ) as the input should be sufficient to learn the hallucination prediction task, we can also easily measure the extent to which including the true target T in the input could help the model. At test time, when evaluating the faithfulness of the machine outputs G, we do not use the true target T and perhaps surprisingly find our model can generalize well without references, even when it was present during training.
To prevent the model from overly relying on the true target T and learning spurious correlations (e.g. the edit distance), we explored two techniques: (1) dropout -randomly drop out tokens in T to force the dependence on the source input;
(2) paraphrase -recall that at synthetic data generation time, we generate T from BART conditioned on the noised T . Instead, we can apply noise functions to the paraphrased sentence of T . Let D denote the paraphrased sentence of T and D denote the generation from BART conditioned on the noised D. Then we create pseudo labels of D denoted L D by computing the edit-distance between the D and D and use ((S, T, D ), L D ) as the training data for finetuning. Since the pseudo labels are created based on D, it can prevent the model from learning the edit-distance between T and D easily. Ablation studies will be provided in § 6.4.
We also add the masked language model loss (MLM) L mlm following (Devlin et al., 2019) . To learn this loss, we create a different batch from the above by concatenating only the source S and target T as the input, since the hallucinated target T could provide erroneous information for predicting masked words in T . We find that such multi-task learning objective helps learn better representations of the inputs and further improves performance on predicting hallucination labels. The final loss is L = L pred + α · L mlm where α is a hyperparameter.
We evaluate hallucination in conditional neural sequence models on abstractive text summarization and machine translation (MT) tasks, using the models and datasets described below. 1 Figure 4 : Finetuning XLM-Roberta (for cross-lingual generation task, e.g. machine translation) or Roberta (for monolingual generation task, e.g. text summarization) on the synthetic training data. 2020)). We post-processed their human annotations by majority voting and create our test datasets for each of the abstractive summarization systems.
Previous work (Wang & Sennrich, 2020; Müller et al., 2019; Koehn & Knowles, 2017) has shown that translation models are particularly prone to hallucination when tested out of domain. We similarly focus on this evaluation regime and additionally consider the low resource case where a modest amount of out of domain data is available at training time. Fleiss's Kappa scores (↑): agreements on token-level hallucination labels or sentence-level (sent) ratings among different annotators. The token-level agreements for XSUM are computed on the released annotations by Maynez et al. (2020) .
Data We use a multi-domain Chinese-English (Zh-En) translation dataset (Wang et al., 2020b) which consists of four balanced domains: law, news, patent and subtitles. We create a new training data D train with law (1.46M sentences), news (1.54M), subtitles (1.77M) train data and randomly sample 0.03% parallel sentences from the patent (870) training data. We train two NMT models (described below) on this dataset and test on 150 examples sampled from patent test data. In addition, we also evaluate the trained models on the COVID-19 domain. We sample 100 examples from the recently released translation benchmark dataset (Anastasopoulos et al., 2020) . Together with the 150 examples from patent test data, we create our benchmark dataset with 250 examples in total and we denote this dataset D eval . We ask human annotators to evaluate the level of hallucinations on this dataset D eval .
Models Our evaluation data is generated from two reference models on which we will measure hallucination (see Appendix for more details): (1) TranS2S (Vaswani et al., 2017) is the standard Transformer Seq2Seq model with 6 encoder layers and 6 decoder layers.
(2) MBART is a Seq2Seq denoising auto-encoder pretrained on large-scale monolingual corpora in many languages. We finetune a MBART checkpoint on D train which is a standard Transformer architecture with 12 layers of encoder and 12 layers of decoder pretrained on a multilingual corpora of 100 languages.
In the human evaluation, three bilingual annotators were presented the Chinese source sentence, the English reference sentence and the machine translation. We shuffled the orders of sentences so that annotators did not know which translation model was used (TranS2S or MBART). We conducted the pilot study and practice sessions with annotators before annotating the blind test set D eval . Annotators were asked to only focus on the hallucinations during assessment and label each sentence with one of the three types of labels: incomprehensible, faithful, and hallucinated. If the translation is a hallucinated one, we asked the annotators to tag all the tokens that were not faithful to the source. We dropped all the translations that were labeled as incomprehensible. The final benchmark datasets were created by taking majority labels among three annotators. (See Appendix A for more annotation details.)
In Tab. 1, we show the Fleiss's Kappa scores of our annotations for MT and the processed annotations from (Maynez et al., 2020) on abstractive summarization. A higher Fleiss's Kappa score indicates higher agreements among annotators. 3 . We achieved moderate agreement on the token-level hallucination annotations and substantial agreement on the sentence-level annotations, while Maynez et al. (2020) achieved substantial or almost perfect agreement on the XSUM dataset. For MT, it's relatively harder to achieve consistent agreements among annotators for the following reasons: first, although we have made strict annotation guidelines following the definition of hallucination in Section 3, it could still be difficult for annotators to distinguish between bad translations and hallucination; second, it was sometimes difficult for annotations to understand the specialized text in the patent domain, which can contain complex scientific descriptions.
We use a pretrained BART model in the fairseq toolkit with 12 layers of encoder and 12 layers of decoder for synthetic labeled data generation. We uniformly sample the percentage of tokens p m to mask from [0, h m ] for each sentence. We also uniformly sample the probability of replacing a token with a random token from [0, h r ] denoted p r . p m and p r are two important factors that affect the noise level when generating the synthetic data. For MT, we set h m and h r to 0.6 and 0.3 respectively. For abstractive summarization, we use 0.4 and 0.2. We use beam search for decoding from BART with beam size of 4 and length penalty of 3. For MT, we first create paraphrased target sentences D through knowledge distillation (Kim & Rush, 2016 ) by using the outputs from the same trained TranS2S model on the source inputs.
Finetuning Pretrained LM For MT, we finetune XLM-R (Conneau et al., 2020) on the synthetic dataset with batch size of 128, and we annotated 50 examples (different from those in D eval ) from the patent test data as the validation dataset. For summarization, we finetune RoBERTa (Liu et al., 2019) with batch size of 96 and early stop training with 10K update steps. In addition, we dropout tokens from the reference T in the input with a rate of 0.5 and 0.3 respectively for summarization and MT to learn L pred . We set α to be 0.6 for MT and 0.5 for summarization based on the scales of L pred and L mlm . For both tasks, we set the mask probability used for L mlm to be 0.3, and the initial learning rate to be 2e − 5 with polynomial decay. We describe other hyperparameters, including training of MT models, in the Appendix B.
MT Table 4 : Performance on the TranS2S benchmark from MT and summarization by using different data as the input to the noised function N (·). "raw" refers to the original targets in the training data.
Evaluation on token-level hallucination prediction In Tab. 2, we present the precision, recall and F1 of token-level hallucination labels across six benchmark datasets for MT and astractive summarization. We achieve descent performance on this task, however it is still far from being solved. Token-level hallucination prediction requires deep semantic understanding of the inputs and is worthy of study in the future. We can see that even though our model learns hallucination prediction with reference T during training (Sec. 4.2), by applying token dropout to T , our model generalizes well without feeding the reference at test time. In contrast, we include the reference at test time and observe that the model can achieve a significantly higher recall but worse precision. Although the proposed method performs relatively worse on the benchmark data of MBART, it performs surprisingly well on other data sets given that we did not use any real annotations at training time.
Comparisons on sentence-level hallucination scores We aggregate the token-level predictions to score the level of hallucination of each sentence and compute the Spearman's correlation coefficients between our scores and the human annotations of percentage of hallucinated tokens in a sentence. In Tab. 3, we present two scores from our model: (1) Probability-based score (P) computes the score by averaging the halluciation probabilities across all the tokes in G.
(2) Ratio-based score (R) first predicts the hard labels by taking arg max at each position and then use the percentage of hallucinated labels as the score.
We compare with two baseline metrics that do not require reference as well: (1) Textual entailment prediction model (Entail) has been proposed to evaluate the faithfulness of abstractive summaries (Maynez et al., 2020; Kryściński et al., 2019; Falke et al., 2019) . We adapt it to MT by finetuning XLM-Roberta on the machine translated Zh-En Multi-NLI dataset (Conneau et al., 2018) . For summarization, we use the finetuned Roberta-large on the Multi-NLI dataset . We calculate the entailment probability P e between the source input and the machine output G and use 1 − P e to score its hallucination level (which is better than use of contradiction probability in our experiments). (2) We propose to use a word alignment model (Align) for hallucination assessment. We employ SimAlign (Sabet et al., 2020) , an unsupervised word aligner, that extracts word alignments from similarity matrices induced from pretrained word embeddings. SimAlign is essentially used for crosslingual tasks, and we adapt it to summarization by using embeddings from the pretrained BERT-large (Devlin et al., 2019) . We compute the percentage of tokens in the machine output G that get aligned aligned to the source input to measure the faithfulness of G.
Overall, in Tab. 3 we see that: (1) For MT, we achieve very significant improvements over the baseline metrics. And both entailment and alignment scores correlate very poorly with the hallucination assessments.
(2) For abstractive summarization, our metrics also significantly outperform baseline metrics. The alignment scores stand out as a better hallucination assessment compared to the commonly adopted entailment scores. We also note that for MT our ratio-based scores correlate better with human judgements while for abstractive summarization the probability-based scores have a better correlation. This shows that our model can produce more confident predictions for MT.
Recent work (Maynez et al., 2020) has shown that pretrained models are better at generating faithful summaries as evaluated by humans. We confirmed this trend in machine translation. In Tab. 5, we present the detokenized BLEU scores, the percentage of hallucinated tokens in the evaluation set from human annotations and our model predictions. We can see that translations from MBART contain much less hallucinated contents than that from TranS2S. Moreover, our predictions match the human assessment very well at the corpus level. Effects of including reference at training time Recall that we concatenate the source, reference and machine generation together as the input when learning hallucination predictions (Sec. 4.2). In Fig.5 , we vary the dropout rate of tokens in the reference at training time and evaluate the models on the outputs from the TranS2S model for both tasks, where dropout rate of 1.0 indicates that we do not include the reference at all. First, different dropout rates do not signficinatly affect performance for MT, this is likely because we use the paraphrased target when creating the synthetic data instead of the reference sentences. Thus, the "hallucinated" sentences D from BART do not resemble the reference T as closely as T , and the model will not learn spurious correlations between the T and D .
Second, for summarization we see that applying word dropout is crucial since we have used the reference more directly for generating synthetic data. On the other hand, if reference is removed at learning time (dropout = 1.0), the resulted model performs poorly, which shows that including reference at training time also has positive effects.
Effects of paraphrased data We investigate the effects of using paraphrased data in Tab. 4, where we apply the noise functions to different forms of targets when generating synthetic data. For MT, we create paraphrased targets via knowledge distillation (Kim & Rush, 2016) where we use the output from TranS2S conditioned on the source sentence in the bi-text corpus as the paraphrased target. We can see that with distillation data for synthetic data generation, the model achieves better results compared to using the references. However, note that we need to choose a proper word dropout rate when using the reference-based synthetic data as discussed above.
For abstractive summarization, we create paraphrased data out of an abstractive and an extractive summarization systems respectively. We finetune BART on the bi-text of XSUM and create distillation data from this finetuned abstractive model. For the extractive system, we use the recent proposed MatchSum (Zhong et al., 2020 ) as the distillation model. We see a significant drop in the performance for both of the variants. This likely due to the fact that: (1) it has been shown that abstractive summarization systems are prone to hallucinate contents themselves (Maynez et al., 2020 ), thus we are not able to create reliable pseudo labels based on the generated summaries, and (2) the extractive system generates summaries out of the input article which diverge from the actual abstractive summaries we evaluate on, and the model cannot generalize well under such data shift.
In this work, we proposed a new evaluation task for hallucination detection in conditional sequence generation and created human-annotated benchmark datasets. We also proposed a novel and generalpurpose method to learn this task. In the future, we hope to create a large-scale pretrained evaluation model for any datasets or models to be evaluated. We also would like to extend our method to any data-to-text generation scenarios. We are also interested in investigating how to leverage our detection methods to mitigate hallucination problems in conditional sequence generation.
We asked three bilingual speakers to annotate the Chinese-to-English evaluation set D eval , which is composed of 150 sentences from the test set of Zh-En multi-domain dataset (Wang et al., 2020b) , and 100 sentences from the COVID-19 translation benchmark dataset (Anastasopoulos et al., 2020) -TICO. TICO contains 6 finegrained domains including Wikisource, Wikivoyage, Wikinews, CMU, PubMed and Wikipedia. we randomly sample 25 examples from each of the four domains -Wikisource, Wikinews, CMU and PubMed, use these 100 samples for evaluation. We train standard base Transformer Seq2Seq and finetune the MBART ) model on the training data from D train .
Annotation Guidelines We ask each annotator to read the tokens in the sentence carefully and check if they can be supported by the source sentence:
• If there are tokens (or the entire sentence) that cannot be supported by the source, label all the span(s) with color and mark the sentence as a hallucinated one;
• If the evaluator can not understand the entire translation at all, mark the sentence as incomprehensible;
• If all the tokens in the translation can be entailed from the source, mark the sentence as a faithful one.
Before the annotation work on the final blind test set, we first perform a pilot study on a different evaluation set and analyze the annotation results from each annotators. Then we conduct an education session to make sure that all the evaluators can fully follow the guidelines.
For each token in the evaluation set, we label it by majority voting, i.e. the label that two or more annotators agree on. We also aggregate the evaluation data from Maynez et al. (2020) Tokenization For TranS2S, we first segment the Chinese corpus with an opensource Chinese word segmentation tool (Luo et al., 2019) , then we learn separate BPE vocabularies with 32k merge operations (Sennrich et al., 2016) over the source (Zh) and the tokenized target (En) corpus respectively. For MBART, we directly apply the contained sentence-piece dictionary in the finetuned model to the raw data of Chinese and English corpus.
When finetuning MBART, we use learning rate of 3e-5, and use polynomial decay for learning rate scheduling with warmup updates of 3,000. The effective batch size is 16,384. Dropout is set to be 0.3 and the attention dropout rate is 0.1. The label smoothing is set to be 0.2. We finetune MBart for 60,000 updates.
Decoding After training, we use beam-search with a fixed beam size 5 for all the models and set the maximum generation length to be twice as the source length.
Subword Tokenization Depending on the pretrained model (Roberta / XLM-Roberta) we finetune on, we apply corresponding subword segmentation to the synthetic data set (S, T, T ) and calculate the edit-distance between the T and T at the subword level. At evaluation time, the model predicts the hallucination labels for each subword in the sentence, thus we predict a word to be a hallucination word if any subword of it is predicted as a hallucinated one.
Synthetic data generation There are a couple of hyperparameters of noised functions in the BART implementation . The main noised functions include (1) random masking (randomly replace a word with MASK), (2) random replacement (randomly replace a word with another word in the vocabulary), (3) random insertion of masks. We found that random masking and random replacement are the two key factors affecting the generated sentences and we have provided their settings in the main paper. We apply a random insertion masks rate of 0.2 for all settings. In addition, the noise functions are applied to words instead of spans in our setting.
Finetuning For MT, we finetune a large XLM-Roberta (Conneau et al., 2020) released in fairseq which is trained on 2.5T of filtered CommonCrawl data in 100 languages. For summarization, we finetune a large Roberta on the synthetic data where we truncate articles that exceed 512 tokens (allowed by the Roberta) to be 512. For both models, we use the Adam optimizer (Kingma & Ba, 2014) with β 1 = 0.9, β 2 = 0.98, = 1e − 6 and weight decay of 0.1. We set the masking probability to be 0.35 for the L mlm loss. The dropout and attention dropout rates are set to be 0.1. We adopt polynomial decay for learning rate scheduling with learning rate of 2e-5. 
We present the percentage of hallucinations from the annotations and our model predictions for the Patent and COVID-10 evaluation data respectively in Tab. 7. We see that both the TranS2S and MBART models produce less hallucinations for the COVID-19 domain although it is a complete out-of-domain test set that has not seen in the training data. We suppose that this is because sentences in COVID-19 are more alike the training domains in terms of writing styles which bring positive transfer. We also note that the predictions from our model match pretty well with human annotations for both of the domains.
Examples In Tab. 8, we present some examples of annotations and our model predictions. We can see that our model performs well in general but can be inaccurate in case of spelling errors of the translations. Besides, we also find some annotation errors while our model predicts correctly.
Reference the arrangement pattern of the projections 2 will now be explained with reference to figs. 