… is due to hard tokens, and empirically validate this by using FL. • We propose a more effective
token loss dynamic reweighting (TLDR) method for reducing repetitive generations that is …

… finished, hard tokens are still under-learned, so that repetitive generations are more … token
loss dynamic reweighting (TLDR) that applies differentiable weights to individual token losses…

… are still under-learned after training, making their repetition more likely to happen. This issue
is addressed by token loss dynamic reweighting (TLDR), which applies … utterance gener- …

… , we propose to dynamically select negative tokens based on … ’s utterance and the knowledge
during the generation stage. … Tldr: Token loss dynamic reweighting for reducing repetitive …

… models trained with MLE objective tend to generate repetitive tokens or phrases, we … Our
main idea is to maintain a dynamic list of novel tokens at each decoding step … In summary, …

… at (1) the target token level to discourage the generation of repeating tokens; (2) the copy …
TLDR: token loss dynamic reweighting for reducing repetitive utterance generation. CoRR, …

… of repetitive tokens by contrasting them with positive tokens. … In summary, one can see
that the repetition problem can be better … TLDR: token loss dynamic reweighting for reducing …

… 4a, we observe that repetition penalty tends to reduce percent repetition for fixed cross-…
TLDR: Token loss dynamic reweighting for reducing repetitive utterance generation. arXiv:…

… Token loss dynamic reweighting (TLDR) hypothesizes that … a repetition penalty in the
decoding process to reduce the … for percentage of repeated tokens across different sampling …

… k most likely tokens), they observe repetition feedback loops … of entries to 0, we dynamically
adapt to differing text-lengths. … We expect the filtering to decrease the share of texts without …