| Challenge: | Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. |
| Approach: | They propose a deterministic search algorithm balancing quality and diversity . they investigate the vanilla best-first search algorithm and propose k-k search algorithm. |
| Outcome: | The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use. |
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Massive-scale Decoding for Text Generation using Lattices (2022.naacl-main)
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| Challenge: | Conditional neural text generation models generate high-quality outputs, but often focus on a mode when what we really want is a diverse set of options. |
| Approach: | They propose a search algorithm to construct lattices encoding a massive number of generation options. |
| Outcome: | The proposed algorithm encodes thousands of diverse options that remain grammatical and high-quality into one lattice. |
Determinantal Beam Search (2021.acl-long)
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| Challenge: | a new beam search approach allows for a diverse subset selection process . standard beam search does not encode interactions between candidates . |
| Approach: | They propose a beam search reformulation that casts subset selection as the subdeterminant optimization problem. |
| Outcome: | The proposed method offers competitive performance against other diverse set generation strategies while providing a more general approach to optimizing for diversity. |
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to decode text to the most probable sequence have been proposed to address these challenges by improving coherence, diversity, and resemblance to human-generated text. |
| Approach: | They propose a novel decoding strategy that extends contrastive search by incorporating an adaptive degeneration penalty informed by the model’s estimated uncertainty at each generation step. |
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Fˆ2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax (2020.emnlp-main)
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| Challenge: | Existing methods for text generation do not fully reflect the rich diversity of human language. |
| Approach: | They propose to use F2-Softmax and MefMax to train a balanced frequency distribution using a frequency class-based method. |
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On Decoding Strategies for Neural Text Generators (2022.tacl-1)
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| Challenge: | a recent study suggests that decoding strategies may be more important than the model architecture itself when generating text from probabilistic models. |
| Approach: | They propose to measure changes in attributes of generated text as a function of decoding strategy and task using human and automatic evaluation. |
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Self-Ensemble of N-best Generation Hypotheses by Lexically Constrained Decoding (2023.emnlp-main)
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| Challenge: | Existing studies have improved generation quality by explicitly reranking N-best candidates. |
| Approach: | They propose a method that ensembles N-best hypotheses to improve natural language generation by combining high-quality fragments of N- best hypothese . they use tokens that should or should not be present in the final output as lexical constraints to improve quality of generation. |
| Outcome: | Empirical results show that the proposed method outperforms strong N-best reranking methods on paraphrase generation, summarisation, and constrained text generation. |
RankGen: Improving Text Generation with Large Ranking Models (2022.emnlp-main)
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| Challenge: | Modern language models assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix. |
| Approach: | They propose a 1.2B parameter encoder model for English that scores model generations given a prefix. |
| Outcome: | The proposed model outperforms decoding algorithms on automatic metrics and human evaluations with English writers. |
Speeding Up Entmax (2022.findings-naacl)
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| Challenge: | Recent studies suggest that sparsity is a problem when the trained model is used for inference. |
| Approach: | They propose an alternative to softmax that produces a dense probability distribution but is slower than softmax. |
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A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
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| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
Comparison of Diverse Decoding Methods from Conditional Language Models (P19-1)
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| Challenge: | Conditional language models can generate a diverse set of outputs, but for open-ended tasks, beam search is ill-suited to generating a set of diverse sequences. |
| Approach: | They propose a method where we over-sample candidates and use clustering to remove similar sequences to achieve high diversity without sacrificing quality. |
| Outcome: | The proposed method over-samples candidates and removes similar sequences to achieve high diversity without sacrificing quality. |