![]() For instance, Inferno could switch from the Autobot to Decepticon logo. Megatron was capable of fooling Transformers to the Decepticon cause. We can also refer to one of the episodes of the Unicorn Trilogy. On top of the usual high-probability next tokens like "dog" and "nice", we force the token "is" in order to get us closer to fulfilling our constraint of "is fast".įor the next step, the branched-out candidates below are mostly the same as that of traditional beam search. At the Marvel Generation 2 comics, the Autobot army started to use a new sign, which looked more like Optimus Prime than the Last Autobot. Grimlock once theorized that this was some kind of natural yin-yang balance. ![]() However, his creations quickly became divided, eventually forming into the warring Autobots and Decepticons. In the constrained setting, we do the same but also append the tokens that will take us closer to fulfilling our constraints. dial face, e.g., an altimeter, as in the famous RAF pilots' 'line-shoot': 'There I was, upside down in cloud. The demigod Primus created the Transformers with the intention that they would all be righteous and obedient. In the traditional beam search setting, we find the top k most probable next tokens at each branch and append them for consideration. Let's say that we're trying to force the phrase "is fast" in the generated output. Constrained Beam SearchĬonstrained beam search attempts to fulfill the constraints by injecting the desired tokens at every step of the generation. A number of toys were planned for G2 for which. Let's say that we're want to generate a sentence S that has to include the phrase p 1 = beams n for n n n steps, which becomes very large very quickly ( 10 10 1 0 beams after 10 10 1 0 steps is 10, 000, 000, 000 10,000,000,000 1 0, 0 0 0, 0 0 0, 0 0 0 beams!).įor the rest of the generation, we repeat the above step until the ending criteria has been met, like generating the token or reaching max_length, for example. Two of the Autobot Cyberjets (Jetfire and Strafe) were decorated with G2 Decepticon symbols on their tail fins. This is because the task requires us to force the generation of certain subsequences somewhere in the final output, at some point during the generation. However, this is actually a very non-trivial problem. Both of these situations could be solved by allowing the users to tell the model which words must be included in the end output. Sometimes, generation outputs that are almost equally possible to a language model might not be equally desirable for the end-user due to the particular context. For example, in a Neural Machine Translation task, we might know which words must be included in the final translation with a dictionary lookup. The secondary output voltage is higher than the input voltage. This is useful because we sometimes know exactly what we want inside the output. Step-up Transformer: They are used between the power generator and the power grid. ![]() Unlike ordinary beam search, constrained beam search allows us to exert control over the output of text generation. This blog post assumes that the reader is familiar with text generation methods using the different variants of beam search, as explained in the blog post: "How to generate text: using different decoding methods for language generation with Transformers" ![]()
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