| Challenge: | Using sampling adapters can improve the quality of the generated text. |
| Approach: | They propose a framework for understanding sampling adapters and propose 'sampling adapters' they argue that the shift enforced by them can be viewed as a trade-off between precision and recall . |
| Outcome: | The proposed framework can be used to improve the quality of language models by modifying their distributions to improve their precision and recall. |
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How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study (2025.findings-emnlp)
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| Challenge: | Recent detectors report near-perfect accuracy, often boasting AUROC scores above 99%, but these claims typically assume fixed generation settings, leaving open the question of how robust such systems are to changes in decoding strategies. |
| Approach: | They examine how sampling-based decoding impacts detectability with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. |
| Outcome: | The proposed framework systematically examines how sampling-based decoding impacts detectability, with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. |
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. |
Local and Global Decoding in Text Generation (2024.findings-emnlp)
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| Challenge: | Text generation relies heavily on decoding algorithms that sample strings from a language model distribution. |
| Approach: | They propose to introduce globally-normalised versions of traditional decoding methods and propose an independent Metropolis-Hastings algorithm to approximate sampling from globally-averaged distributions without explicitly computing them. |
| Outcome: | The proposed method approximates the distributions without explicitly computing them. |
Locally Typical Sampling (2023.tacl-1)
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| Challenge: | a discrepancy in probabilistic language generators has puzzled the language generation community for years . |
| Approach: | They propose a method for local typical sampling to enforce a conditional entropy criterion for probabilistic models. |
| Outcome: | The proposed method can provide new insights into why high-probability texts can be dull or repetitive. |
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation (2020.aacl-main)
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| Challenge: | Existing sampling algorithms for auto-regressive language models have similar performance . entropy reduction, order preservation and slope preservation are common properties of existing methods . |
| Approach: | They investigate the quality-diversity trade-off between ancestral sampling algorithms for auto-regressive language models. |
| Outcome: | The proposed methods have similar performance to existing methods for open-ended language generation. |
Truncation Sampling as Language Model Desmoothing (2022.findings-emnlp)
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| Challenge: | Long samples of text from neural language models can be of poor quality. |
| Approach: | They propose to think of a neural language model as a mixture of k and a true distribution that avoids infinite perplexity. |
| Outcome: | The proposed methods generate more plausible long documents according to humans and break out of repetition. |
A Probability–Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors (2024.emnlp-main)
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| Challenge: | a relationship exists between the quality of a string and its probability, p(y), under a language model, and the quality and quality of the string. |
| Approach: | They examine the probability-quality relationship in language models aligned to human preferences through reinforcement learning through human feedback. |
| Outcome: | The proposed method improves the quality of text sampled from a language model by skewing the model towards high-probability strings. |
Min-k Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics (2026.acl-long)
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Yuanhao Ding, Meimingwei Li, Esteban Garces Arias, Matthias Aßenmacher, Christian Heumann, Chongsheng Zhang
| Challenge: | Existing methods for decoding large language models have extreme sensitivity to temperature parameter T. |
| Approach: | They propose a dynamic truncation strategy that analyzes the local shape of the logit distribution to identify "semantic cliffs" they show that Min-k consistently improves text quality even under extreme temperature settings . |
| Outcome: | The proposed method achieves strict temperature invariance and low sensitivity to hyperparameter choices. |
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. |
Top-n𝜎: Eliminating Noise in Logit Space for Robust Token Sampling of LLM (2025.acl-long)
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| Challenge: | Existing sampling methods that are sensitive to temperature scaling fail to distinguish between diversity and noise. |
| Approach: | They propose a method that identifies informative tokens by eliminating noise directly in logit space and a new sampling method that is temperature-invariant. |
| Outcome: | The proposed method outperforms existing methods with significant improvements in reasoning and creative writing tasks. |