| 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. |
| Outcome: | The proposed study shows that decoding strategies do not always transfer across tasks . authors show that the differences in attributes are not always consistent across tasks, they say . |
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| Challenge: | Decoding strategies affect the probability distribution underlying the output of a language model and can therefore affect both generation quality and uncertainty. |
| Approach: | They investigate the impact of decoding strategies on uncertainty estimation in large language models . |
| Outcome: | The proposed methods improve the uncertainty estimation of large language models by reducing repetition. |
How Decoding Strategies Affect the Verifiability of Generated Text (2020.findings-emnlp)
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Luca Massarelli, Fabio Petroni, Aleksandra Piktus, Myle Ott, Tim Rocktäschel, Vassilis Plachouras, Fabrizio Silvestri, Sebastian Riedel
| Challenge: | Recent advances in pre-trained language models have generated text of an increasingly high quality. |
| Approach: | They propose a decoding strategy that produces less repetitive and more verifiable text. |
| Outcome: | The proposed method produces less repetitive and more verifiable text than previously used decoding strategies. |
If beam search is the answer, what was the question? (2020.emnlp-main)
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| Challenge: | surprisingly, beam search results on language generation tasks are low-quality . despite its high error rate, beam searches can be used to decode models with high probability . |
| Approach: | They frame beam search as the exact solution to a different decoding objective . they propose a set of decoding objectives that explicitly enforce this property . |
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Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation (2025.coling-main)
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| Challenge: | Generative large language models generate a high-dimensional probability distribution over all tokens in their vocabulary. |
| Approach: | They conduct extensive sensitivity analyses to determine how hyperparameter choices shape the outputs of generative large language models. |
| Outcome: | The proposed methods influence the distribution of diversity and coherence metrics in human-written text, but the optimal configurations vary across models and tasks. |
Generating Text from Language Models (2023.acl-tutorials)
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| Challenge: | a growing percentage of natural language processing tasks focus on the generation of text from probabilistic language models. |
| Approach: | They will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
| Outcome: | This tutorial will provide a centralized discussion of critical considerations when choosing how to generate from a language model. |
The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
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| Challenge: | Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning. |
| Approach: | They will discuss how and why NLG models succeed/fail at generating coherent text. |
| Outcome: | This paper will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications. |
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. |
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A Thorough Examination of Decoding Methods in the Era of LLMs (2024.emnlp-main)
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| Challenge: | Decoding methods are essential for converting language models from next-token predictors into practical task solvers. |
| Approach: | They propose to evaluate decoding methods in general-purpose large language models . they find that decoding method performance is notably task-dependent . |
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Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)
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| Challenge: | Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions. |
| Approach: | They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features. |
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Automatic Detection of Generated Text is Easiest when Humans are Fooled (2020.acl-main)
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| Challenge: | Recent advances in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. |
| Approach: | They compare decoding methods with popular sampling-based decoding strategies . they show that multi-sentence excerpts can fool expert human raters over 30% of the time . |
| Outcome: | The proposed methods improve with longer excerpt length, but multi-sentence excerpts fool human raters over 30% of the time. |