Neural machine translation (NMT) systems have reached state of the art performance in translating text and widely deployed. Yet little is understood about how these systems function or break. Here we show that NMT systems are susceptible to producing highly pathological translations that are completely untethered from the source material, which we term hallucinations. Such pathological translations are problematic because they are are deeply disturbing of user trust and easy to find. We describe a method t generate hallucinations and show that many common variations of the NMT architecture are susceptible to them. We study a variety of approaches to reduce the frequency of hallucinations, including data augmentation, dynamical systems and regularization techniques and show that data augmentation significantly reduces hallucination frequency. Finally, we analyze networks that produce hallucinations and show signatures of hallucinations in the attention matrix and in the stability measures of the decoder. Neural machine translation (NMT) systems are language translation systems based on deep learning architectures BID10 BID1 BID31. In the past few years, NMT has vastly improved and has been deployed in production systems, for example at Google BID33, Facebook BID15, Microsoft BID17, and many others. As NMT systems are built on deep learning methodology, they exhibit both the strengths and weaknesses of the approach. For example, NMT systems are competitive with state of the art performance BID6 and scale well to very large datasets BID23 but like most large deep learning systems, NMT systems are poorly understood. For example, in many commercial translation systems, entering repeated words many times occasionally in strange translations, a phenomenon which has been highly publicized BID12. More broadly, recent work shows that NMT systems are highly sensitive to noise in the input tokens BID3 and also susceptible to adversarial inputs BID9. When there is an error in translation, it can be challenging to either understand why the mistake occurred or engineer a fix. Here we continue the study of noise in the input sequence and describe a type of phenomenon that is particularly pernicious, whereby inserting a single additional input token into the source sequence can completely divorce the translation from the input sentence. For example, here is a German input sentence translated to English (reference) by a small NMT system: Source: Caldiero sprach mit E! Nachrichten nach dem hart erkämpften Sieg, noch immer unter dem Schocküber den Gewinn des Großen Preises von 1 Million $. Reference: Caldiero spoke with E! News after the hard-fought victory, still in shock about winning the $1 million grand prize. NMT Translation: Caldiero spoke with E, after the hard won victory, still under the shock of the winning of the Grand Prix of 1 million $. Since its invention, researchers have been working to better understand NMT. For example, moving from the original Seq2Seq model BID31 BID11 to models that utilize attention mechanisms BID1, ed in improved translation quality BID22 and better interpretability BID14. Studies identified the most critical components of the LSTM BID16 and the role of the LSTM in language modeling BID19 more broadly. Followed by explorations in interpretability, recent work has focused on robust NMT, studying the effects of input noise, aiming to reduce variation from typos BID3 and synonym choice BID9 in the discrete input set used by NMT systems. Both BID3 and BID9 have discovered that NMT systems are highly sensitive to input noise and both used adversarial training to help stabilize NMT systems (either with black-box adversarial training or augmenting an adversarial loss). There has been work in understanding how to handle some of the pathologies in RNNs for language modeling and translation, for example, using scheduled sampling to handle mismatches between training and test sets BID5.In parallel, there has also been work in understanding RNNs through the framework of dynamical systems BID30 BID28 BID21 BID25 BID2 and understanding their capacity in language tasks BID13. For example, it is known that continuous time vanilla RNNs exhibit high-dimensional chaos BID27, which can be driven out by strong input BID24. RNNs exhibiting chaos can be beneficial, for example, RNNs capable of chaos in the absence of input can serve as strong initialization for training RNNs when they are input driven BID29, but caution must be used as unbridled chaos can be viewed as a source of dynamical noise. This has led to efforts to rid RNNs of chaos altogether BID21. Efforts related to improving optimization may also have dynamically regularizing effects, e.g. BID0. Given the complexities and slow compute times of recurrent systems, there have also been attempts to rid NMT of recurrence BID18 BID15 BID32.We further expand on these studies by highlighting the specific pathology of hallucinations, systematically studying those hallucinations, and analyzing them from a dynamical systems perspective. Models: In this paper, we use a standard RNN-based encoder-decoder NMT model with attention. Specifically, we study the NMT model described in BID33, known as GNMT. We use the GNMT model and its public implementation 2. Formally, given an input sequence, x 1:S of length S, the NMT model first encodes the input sequence x 1:S into a set of vectors z 1:S = f enc (x 1:S) using its encoder f enc. The task of the decoder f dec, is to generate the translation, y 1:T, one symbol at a time y i, given the encoding, z 1:S, and previously generated symbols y <i. The decoder, f dec is implemented as a conditional sequence model BID1, where the distribution over y 1:T is conditioned on x 1:S. The decoder internally makes use of an attention mechanism f att to query the encoder, summarizing z 1:S for each output symbol y i, putting it all together y i = f dec (y < i, f att (z 1:S)) (also see Figure 7 for a detailed model schematic). Finally, the conditional probability of the target sequence is modelled as p(y 1:T |x 1:S) = T i=1 p(y i |y < i, x 1:S) and the log of this conditional likelihood is maximized given a set of source-target pairs (x, y) during training. We study models that are significantly smaller and less complex than those typically used in state-ofthe-art or production systems for the sake of research tractability. We use a single layer bidirectional LSTM in the encoder f enc and a two layered unidirectional LSTM in the decoder f dec with an additive attention mechanism as f att BID8. The word embedding dimensions and each LSTM hidden cell (both in the encoder and decoder) are set to 256. We refer to this model as the canonical model. Unless otherwise stated, we used Adam BID20 optimizer with a learning rate of 0.001, a constant learning rate schedule, and clipped gradients to a maximum norm of 5.0 during training. Given these hyper-parameter and architectural choices, we trained 10 canonical models with different random seeds to observe how parameter initialization variability played a role in our . All additional model variants we describe later were also trained 10 times with the same 10 different random seeds. Each model was trained for 1M steps (updates) with a mini-batch of size 128 and the training checkpoint with the best BLEU score on the development set was selected. The central goal of our study was to understand how various modelling choices affected the frequency of hallucinations. In order to isolate the effects of modeling changes, all model variants we study in this paper were identical to the canonical model except for a single change. This means, for example, that our model with 512 hidden units also is 2 layers deep, etc. We performed a simple hyper-parameter search for the canonical model, and did not perform additional hyper-parameter searches for any additional models. All models we present are well trained with a BLEU score of at least 20.0 on the test set using greedy decoding, a reasonable score for 2-layer models with 256 hidden units. With beam search decoding, our canonical models achieve an average BLEU score of 25.66.Inference: Generating a translation of the input sequence, or formally finding an output sequence that maximizes the conditional log-probability,ŷ = argmax y log p(y|x), is a major challenge in NMT since the exact inference (or decoding) is intractable. NMT uses approximate decoding techniques which we also have used in this paper. The simplest approximate decoding technique, greedy decoding, chooses the most-likely symbol under the conditional probabilitŷ y t = argmax i log p(y t = i|ŷ <i, x 1:S), outputting a single best local prediction by keeping track of a single hypothesis k, at each time step. Another approximate decoding technique, beam search, improves upon greedy decoding by keeping track of multiple hypotheses (beams), where k >1 at each time step of the decoding, compared to k=1 in greedy-decoding. To maintain simplicity in our canonical model we used greedy decoding. Note that production systems will often perform beam search to find a more probable translation than one generated by greedy search. We also ran an additional set of experiments with beam search. We trained all models with the German to English WMT De→En 2016 dataset (4,500,966 examples) BID7, validated with the WMT De→En 2015 development set (2,169 examples). We then used the WMT De→En 2016 test set (2,999 examples) to compute the hallucination percentage for each model. For the input and output of all NMT models in consideration, we used sub-word tokens extracted by Byte-Pair Encoding (BPE) BID26. To construct a vocabulary of unique tokens, we first combined the tokenized source and target corpora BID2, and then learned a joint BPE code with an 8k merge operations budget, ing in 12,564 unique tokens. Further, in order to study the effect of larger vocabulary sizes, for some experiments we repeated the same process with 16,000 and 32,000 BPE codes and ended up with vocabularies having 19,708 and 36,548 unique tokens respectively. Note that we used the same vocabulary for both source and target side languages. Computing the percentage of hallucinations in a NMT model Select a model; Fix a random seed; Select a group of subword tokens with the following attributes:; -Common tokens: 100 most common subword tokens; -Mid-frequency tokens: random sample of 100 subword tokens between common and rare tokens; -Rare tokens: 100 least common subword tokens; -Punctuation: all punctuation tokens; for every sentence in test corpus (e.g. WMT De→En 2016 test set) do if adjusted BLEU between reference sentence and translated sentence > 0.09 then for every selected token do for every location in (beginning, end, second-to-end, randomly in the middle) do put the selected token at the selected location in the byte-pair encoded input sequence; translate the perturbed input sequence; if adjusted BLEU between the translated, perturbed sentence and the translated, unperturbed sentence< 0.01 then this sentence can be perturbed to produce a hallucination; Informally, a hallucination is a translation of a perturbed input sentence that has almost no words in common with the translation of the unperturbed sentence. Here, we use the term perturb to mean adding a single token to the input source sequence. This is based on an initial observation that adding a rare token to an input sequence reliably caused a model to generate a hallucination, e.g. adding a Chinese character token to a German to English translation. We expanded and systematized this discovery into a brute force search procedure (Algorithm 1) by splitting our tokens into several types: common (100 most common German tokens), rare (100 least common German tokens), mid-frequency tokens (randomly sampled 100 tokens from the remaining German tokens), and punctuation tokens. Additionally, we attempted to perturb each sentence by inserting a token at one of several positions: beginning, end, second to the end, and randomly in the middle. We did this for every sentence in our test set and collected statistics for each model variant. To define a quantitative threshold for a hallucination we modified the BLEU score, which is used to compare a reference sequence with a translated sequence. Briefly, the BLEU score is a common metric for translation, which measures weighted n-gram overlaps while penalizing short translations. We modified the BLEU score by re-weighting the n-grams in the BLEU score computation to favor having any words in common between the two sentences (1.0 for one-grams and 0.8 for bi-grams and disregarded other n-grams). Then, we call only sentences that have an adjusted BLEU score of less than 0.01 hallucinations. For examples of sentence pairs with different adjusted BLEU scores, see Section 8.2 in the Appendix. Not all translations are good even before adding a perturbation token to the input sequence. To strengthen our on hallucinations, we first excluded these poor translations by computing the adjusted BLEU score between the reference translation and the translation produced by the unperturbed input sequence. We kept sentences that had an adjusted BLEU of ≥ 0.09. We chose a value of 0.09 because it seemed to maintain enough context that you could tell the translation and the reference were related. We describe four common hallucination patterns: grammatically correct output that bears no relation to the input text, ungrammatical output with oscillatory structure, output that remains largely in the source language, and finally terse jumps to the end of the sequence. We also observe translations that are ungrammatical nonsense. While still highly undesirable, we note that a user should at least be able spot and reject these additional hallucination patterns. We show that hallucinations can be easily evoked by inserting tokens in the source sequence. We used Algorithm 1 to quantify how susceptible a given model is to hallucination. In particular, we studied what types of perturbations (location, and token type) are more effective at inducing hallucinations. With this method, we found that, on average, 73% of all sentences in the WMT De→En test set can be perturbed to hallucination in the canonical model. We studied how beam search, number of hidden units, vocabulary size, and decoding scheme affected hallucination percentages FIG0. We found that changing the number of hidden units to both 512 and 1024 from 256 and changing the vocabulary size-from 8K to 16K BPE codes did not significantly decrease the hallucination percentage. However, beam search and a vocabulary size increase corresponding to 32K BPE codes did significantly lower the mean percentage of hallucinations. We also studied how different types of perturbations impacted the hallucination percentage of the canonical model FIG0. By far, adding a perturbing token to the beginning of the input sequence induces the most hallucinations in the canonical model. We were curious if BLEU scores were predictive of hallucination percentage. We plot the BLEU vs. hallucination percentage of all models we study (FIG1 . Surprisingly, we found that hallucination percentage does not decrease as BLEU score increases. However, since we did not study all possible models, we urge caution in interpreting these . What changes can we make to the model to make it more robust to hallucinations? We investigated the effect of three different methodologies, simple regularizations, data augmentation and regularizations on the dynamics in state space. We tested if a model variation significantly reduces hallucinations by performing a one-sided Mann-Whitney U between the canonical distribution of models and the distribution of models that use the model variant. We use a p-value of 0.05.Simple Regularizations: We choose dropout, L2 regularization on embeddings (L2E), L2 regularization on recurrent weights (L2R) and L2 regularization on all weights (L2) as straight-forward regularization techniques to be applied. For dropout, we created a model with dropout in all feedforward layers, with a keep probability of 0.9. Next, we implemented L2 regularization on the We augmented the training data by perturbing all training sentences with a random token (either common, rare, mid-frequency, or punctuation) at either the beginning, end, second-to-end, or randomly in the middle while keeping the reference translation the same. This doubled our training set. We then trained a canonical model with the augmented training set, and found that data augmentation helps decrease hallucination percentages. We call this model DA.Dynamical Regularizations: We wondered if hallucinations could be reduced by providing a more exact initial state for the decoder, so we trained additional models where the initial state of the decoder was tied to last step of the encoder (TDIS). Note that the canonical model sets the decoder initial state as a vector of zeros. As a second regularization method that operates on the state space, we used Chaos-free network (CFN) BID21 which by premise cannot produce chaos. We replaced the LSTM cell with the CFN in a set of experiments, again using 256 hidden units. Dropout, L2E, and DA all ed in statistically significant decreases in hallucination percentage, with DA being by far the most effective at decreasing hallucinations. On the contrary, switching out Although data augmentation dramatically reduced hallucinations in the canonical model, it requires knowing the kind of perturbations that one would use to induce a hallucination. To study how fine grained one's knowledge must be, we trained the canonical model on a different training set where we withheld two types of data augmentation: perturbing at the beginning or with common tokens (We call this model DA w/o beginning or common). We then compared this model with the canonical model trained with the full DA training set (Figure 4). We found that DA w/o beginning or common yields much higher hallucination percentages when tested by perturbing at the beginning or with common tokens in comparison to the DA model. However, we also saw a reduction in hallucination percentage for common and beginning tokens when compared to the canonical model. This indicates that DA can still provide some protection against hallucinations, even if exact perturbations are not known. Figure 4: Effects of data augmented (DA) training when including all perturbation types vs excluding common and beginning perturbation types. We trained two models, one including all perturbations types for DA training, and the other excluding common and beginning perturbation types. We then examined the hallucination percentage of each perturbation type for both of these models and studied whether a DA model would be less prone to hallucinate when perturbed with types of tokens or positions it had not been trained against. Red star shows that DA w/o beginning or common had statistically significantly reduced mean compared to the canonical model trained without DA.Additionally, we wondered if hallucinations were present in NMT architectures that were not recurrent. Thus, we studied the Transformer model (TR) BID32. To make our easily accessible to NMT practitioners, we chose a hyperparameter set from those given in the popular Tensor2Tensor library BID3 that was closest to our cutoff BLEU score when using greedy decoding. These models are trained with the parameters from transformer tiny (2 hidden layers, 128 hidden size, 512 filter size, and 4 heads) and have a greedy BLEU score of 17.5, which is a little lower than our GNMT models. We find the transformer model hallucinates significantly less than the canonical model, but can still be perturbed to hallucinate on average 15% of the time FIG2 ). We present these with many caveats. Unlike the canonical model, this model is trained with many types of regularization (dropout, attention dropout, label smoothing, relu dropout, and a larger batch size) and a longer input sequence length (256 versus 50 in the canonical model). Unfortunately, training with no regularization or a sequence length of 50 dramatically reduced the BLEU score for parameter combinations we tried, and thus we decided to present these with caveats instead of a model without regularization and a comparable sequence length. We observed a large difference between attention matrices of normal translations and of hallucinations. Attention networks in normal translations tend to study the entire input sequence throughout decoding. In French to English and other language pairs that are grammatically aligned (German to English is somewhat aligned), this often in a strong diagonal in the attention matrix. The attention matrix, when translating hallucinations, however, shows the model attending to a few tokens. We give an example comparison in FIG3, top panels. For additional attention matrices, see Section 9.We wanted to quantify this difference in a way that does not require strong alignment between languages, i.e. one expects English to French to in a largely diagonal matrix but not English to Turkish, so we used information entropy to compute a statistic that described the difference between attention matrices during a decode that ed in a hallucination and those that ed in a normal translation. Specifically, at each output of the decoder, the attention network gives a distribution over input tokens. We averaged these distributions across all decoded output tokens, ing in a distribution of average attention weight over the input tokens. We treated this as a discrete distribution and computed the entropy, − t p(x t) log p(x t), where x t is the input token at time t, for each example, ing in a distribution of entropy values over all decoded sequences. We then compared the entropy of average attention distributions between hallucinations and correct translations (FIG3 . This figure shows a significant difference between the entropy values for hallucination sequences. As a control, we show there is no significant difference between original input sequences and perturbed input sequences for sentences that cannot be perturbed to hallucination. Note that, in real world scenarios where a ground truth translation is not available, the measure of entropy of the average attention distribution may be useful to detect hallucinations. The breakdown of the attention module seems to signal that the encoder and the decoder have been decoupled, and the decoder ignores context from the encoder and samples from its language model. Two possibilities are that broken attention modules are the root cause of decoupling, or they are a symptom of further breakdown in the dynamics of the decoder. Examining the causes and types of hallucinations: instability of translation, translations decoupled from the input, and oscillations in translations, led us to believe that hallucinations from a dynamical process gone wrong in the decoder (which might be caused by the encoder or attention module). Further, many model variants that decrease hallucinations (like L2 regularization) can be viewed as regularizing the dynamics of the model. Thus, we explore differences in the decoder between translating a hallucinating or non-hallucinating sentence by comparing the hidden states of the decoder and analyzing the stability of the system FIG4 ).In this section, we are interested in how perturbations change the decoding pipeline and thus study an unchanged, input sentence (which we call "original" and denote by x o) and its perturbations (x p). We perturbed all source sentences in the test set with all types of tokens (common, rare, mid-frequency, and punctuation) at all locations (beginning, end, second-to-end, and randomly) and sorted all perturbations into two groups: those that in hallucinations and those that do not. We hypothesize that differences in translation occur early in decoding and focus on the first timestep the decoder receives context from the encoder, t = 1. We studied both the distance between (left panel) and ratio of the norms (middle panel) of the perturbed decoder hidden states h 1 (x p) and the original decode, h 1 (x o), at decode t = 1. Both ed in obviously different distributions as a function of whether the perturbation ed in a hallucination. Given these differences (FIG4), it seemed natural to attempt to causally reduce the number of perturbations by re-normalizing h 1 (x p) to the norm of h 1 (x o). We tried this for all perturbations of all original sentences in our test set and did not see a reduction in hallucination percentage. We wondered if a hallucination from exciting an unstable mode of the decoder early in the decode. To study this, we defined an unstable subspace of the original decode U (x o) as the subspace spanned by the eigenvectors of DISPLAYFORM0 ∂h0 (x o) with corresponding eigenvalues greater than 1. We projected the normalized hidden state of the perturbed input, x p, onto this subspace A. Normal translations produce attention matrices that show a distribution of weight across most tokens in the source sequence throughout decoding (x-axis source sequence, y-axis decoded sequence). B. However, during hallucinations, the attention network tends to place weight on only a few input tokens. We can see the majority of the weight throughout decoding is placed on the "." in the source sequence. C, D. We used an entropy measure to quantify how distributed the attention is over the input source sequence. Shown are the distributions for normal translations C. and hallucinations D. for the original (blue) and perturbed (green) attention entropy values. Mean of the entropy distributions for hallucinations are statistically significantly different (Mann-Whitney U test, p < 0.05). DISPLAYFORM1 whereĥ is h normalized to 1. We did this for every original sentence in our test set that had at least 10 hallucinations (around half of all original sentences). We show the count (up to 10) of perturbed sentences such that E(x o, x p) > 1 when x p ed in a hallucination (red) and when it did not (blue) in FIG4 (right panel).Finally, we also studied the stability exponents of the decoder, focusing on how eigenvalues of the Jacobian, ∂h T ∂h0 (x), of the hidden states of the decoder changed as a function of whether or not a perturbation ed in a hallucination for models trained with and without data augmentation (Shown in Appendix 10). In this paper we uncovered and studied a hallucination-like phenomenon whereby adding a single additional token into the input sequence causes complete mistranslation. We showed that hallucinations are common in the NMT architecture we examined, as well as in its variants. We note that hallucinations appear to be model specific. We showed that the attention matrices associated with hallucinations were statistically different on average than those associated with input sentences that could not be perturbed. Finally we proposed a few methods to reduce the occurrence of hallucinations. Our model has two differences from production systems. For practical reasons we studied a small model and used a limited amount of training data. Given these differences it is likely that our model shows more hallucinations than a quality production model. However, given news reports of strange translations in popular public translation systems BID12, the dynamical nature of the phenomenon, the fact that input datasets are noisy and finite, and that our most effective technique for preventing hallucinations is a data augmentation technique that requires knowledge of hallucinations, it would be surprising to discover that hallucinations did not occur in production systems. While it is not entirely clear what should happen when a perturbing input token is added to an input source sequence, it seems clear that having an utterly incorrect translation is not desirable. This phenomenon appeared to us like a dynamical problem. Here are two speculative hypotheses: perhaps a small problem in the decoder is amplified via iteration into a much larger problem. Alternatively, perhaps the perturbing token places the decoder state in a poorly trained part of state space, the dynamics jump around wildly for while until an essentially random well-trodden stable trajectory is found, producing the remaining intelligible sentence fragment. Many of our can be interpreted from the vantage of dynamical systems as well. For example, we note that the NMT networks using CFN recurrent modules were highly susceptible to perturbations in our experiments. This highlights the difficulty of understanding or fixing problems in recurrent networks. Because the CFN is embedded in a larger graph that contains an auto-regressive loop, there is no guarantee that the chaos-free property of the CFN will transfer to the larger graph. The techniques we used to reduce hallucinations can also be interpreted as dynamical regularization. For example, L2 weight decay is often discussed in the context of generalization. However, for RNNs L2 regularization can also be thought of as dynamically conditioning a network to be more stable. L2 regularization of input embeddings likely means that rare tokens will have optimization pressure to reduce the norm of those embeddings. Thus, when rare tokens are inserted into an input token sequence, the effects may be reduced. Even the data augmentation technique appears to have stability effects, as Appendix 10 shows the overall stability exponents are reduced when data augmentation is used. Given our experimental , do we have any recommendations for those that engineer and maintain production NMT systems? Production models should be tested for hallucinations, and when possible, the attention matrices and hidden states of the decoder should be monitored. Our on reducing hallucinations suggest that standard regularization techniques such as Dropout and L2 weight decay on the embeddings are important. Further, data augmentation seems critical and we recommend inserting randomly chosen perturbative tokens in the input sentence as a part of the standard training regime (while monitoring that the BLEU score does not fall). We note a downside of data augmentation is that, to some extent, it requires knowing the types of the pathological phenomenon one desires to train against. Figure 7: Schematic of the NMT decoder. The input sequence, x 1:S, is encoded by a bidirectional encoder (not shown) into a sequence of encodings, z 1:S. The attention network, f att, computes a weighted sum of these encodings (computed weights not shown), based on conditioning information from h and provides the weighted encoding to the 2-layer decoder, f dec, as indicated by the arrows. The decoder proceeds forward in time producing the translation one step at a time. As the decoder proceeds forward, it interacts with both the attention network and also receives as input the decoded output symbol from the previous time step. Examples of pairs of sentences with different adjusted BLEU scores are as follows: As seen above, an adjusted BLEU score of < 0.01 means the two sentences have very few words in common. DISPLAYFORM0 We defined a spectrum of stability exponents for the decoder and compared them between normal translations and hallucinations (FIG6). Concretely, we studied the stability of the decoder as a function of a given input token sequence, x 1:S of length S (denoted x below). The sequence x 1:S is run through the encoder, whose output is processed by the attention network, finally delivering an input to the decoder. For a given input token sequence, the decoder runs until it produces an endof-sequence token, ing in an output token sequence y 1:T of length T (or reaches a maximal decoded sequence length T > 3S). We were interested in studying DISPLAYFORM0 ∂h0 as many stability properties can be deduced from it. We note that if one is interested in studying ∂ht ∂xs, the iterative process described by ∂h T ∂h0 would still be critical to understand due to the chain rule. We defined our spectrum of stability exponents, in analogy with Lyapunov exponents, but adapted for finite time by studying a finite-time version of Oseledets matrix, typically used in the study of chaotic dynamical systems. In particular, the i th stability exponent is defined as λ i (x) = 1 2T log (α i (x)), where α i (x) is the i th eigenvalue of the positive-semidefinite symmetric matrix DISPLAYFORM1 ∂h0 (x) and h(t) is the decoder state at time t concatenated across all layers. We used auto-differentiation software to exactly compute the Jacobian ∂h T ∂h0 (x) so the complexities of the decoder circuitry were handled naturally (shown in Appendix, Section 8.1 BID4 .We show the distribution of stability exponents, comparing between all input sequences that could be made to hallucinate, and all those that could not ( FIG6). We show these for both the canonical model and the model trained with data augmentation. There are two observations. First, means of the distribution of the stability exponents for the canonical model, averaged over those sentences that could be perturbed to hallucinate, are statistically different than exponents averaged over sentences that could not be perturbed to hallucinate. Second, the distributions of the model trained with data augmentation show significantly reduced exponents in comparison to the canonical model.
We introduce and analyze the phenomenon of "hallucinations" in NMT, or spurious translations unrelated to source text, and propose methods to reduce its frequency.