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Some Natural Language Generation (NLG) tasks require both faithfulness anddiversity. The decoding strategy is intensively related to the quality of thegenerated text. Strategies such as beam search, greedy search, etc., performwith low diversity and high repetition. On the other hand, guided decoding, thesolution towards diversity, may generate unfaithful expressions. To this end,this paper presents Information Filter upon Diversity-Improved Decoding (IFDID)to obtain the tradeoff between diversity and faithfulness. IFDID is a two-stagedecoding strategy leveraging the proposed Enhance-Filter framework, whichachieves the tradeoff by increasing the probabilities of some typical tokensbeing selected and subsequently filtering them by their information amount. Toverify the effectiveness, we compare our method with other baselines on relatedCommonGEN, RocStories and AdGen benchmarks, which cover Chinese and Englishdatasets. Our numerical experimental results and human evaluation outcomesverify the effectiveness of the proposed approach, as our approach achieves a1.24 higher ROUGE score describing faithfulness as well as higher diversityrepresented by 62.5% higher upon Dist-2 than traditional approaches,demonstrating that IFDID is a novel SOTA decoding strategy for the tradeoffbetween diversity and faithfulness.

IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG