This simple generative task requires a model to copy binary symbols over long-range dependencies, ignoring the tokens in between. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques.

This simple generative task requires a model to copy binary symbols over long-range dependencies, ignoring the tokens in between. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques.

A.1 Summary of Commonly Used Metrics for T ext Generation Table 1: Summary of commonly used metrics for text generation. For settings and tasks, we only list the ones justified by the original paper for each metric. We conduct experiments on WMT19, and the results are shown in Tab. 2. We don't observe A.3 Prompt Set In Tab. 3, we list the full prompt set for both s h direction and h r direction. Prompt Set s h Last Tersely Succinctly In summation To put it succinctly After In brief All in all To summarize Bringing up the rear Behind In short In outline In a nutshell To come to the point Lastly Concisely In closing In conclusion In the final analysis In sum In precis In passing In winding up Without wasting words To end In a word To conclude Last in order At the end of the day Curtly Compactly Summarising In a few words Without waste of words Crisply Summarily In the rear As a final point Finally yet importantly At last To sum up Summarizing Not least of all To put it in a nutshell Pithily Basically Laconically To put it briefly When all is said and done Shortly In the end At the rear Not to mince words To cut a long story short In fine At the end To be brief Last but not least Not to beat about the bush Finally In essence Last of all Just as importantly In drawing things to a close Briefly Ultimately Elliptically To put it concisely Not to put too fine a point on ith r As To wit As it were Case in point As an illustration sc. That is Especially That is to say To give an example i.e.

This design preserves the structural properties of the source domain, thereby ensuring that the end-model upholds them.

Thispaperproposes a new quasi-attention that is compositional in nature, i.e., learning whether to add, subtract or nullify a certain vector when learning representations. This is strongly contrasted with vanilla attention, which simply re-weights input tokens.

First, we analyze token distributions generated across multiple prediction heads.

Sequence-to-sequence learning with neural networks has become the de facto standard forsequence prediction tasks.