Papers with sampling methods
How Relevant is Selective Memory Population in Lifelong Language Learning? (2022.aacl-short)
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| Challenge: | Existing approaches to lifelong language learning rely on sparse experience replay to prevent catastrophic forgetting. |
| Approach: | They propose to use a selective memory population to store a uniform number of samples from the entire data stream to improve model performance. |
| Outcome: | The proposed methods show that they are relevant for lifelong language learning tasks, especially for low memory size, and consistent with computer vision studies. |
Reverse Engineering Configurations of Neural Text Generation Models (2020.acl-main)
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| Challenge: | Recent advances in neural text generation modeling have raised concerns about how such approaches might be used in malicious ways. |
| Approach: | They propose to distinguish which of several variants of a given model generated some piece of text by performing diagnostic tests. |
| Outcome: | The proposed method identifies which of several variants of a given model generated some piece of text and if so, if it is more sensitive to different modeling choices than previously thought. |
Multimodal Generation with Consistency Transferring (2025.findings-naacl)
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| Challenge: | Existing methods for multimodal content generation are limited to unimodal content production due to high training complexity, significant costs, and inadequate emphasis on model constraints. |
| Approach: | They propose a method to generate multimodal content with constraints on adjacent steps and a layer-based layer-constrained transfer between adjacent steps to improve denoising capabilities. |
| Outcome: | The proposed method improves the model’s ability to capture actions and depict backgrounds more effectively and improves video generation speed by approximately 40% and quality by about 39.3%. |
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. |
On the True Distribution Approximation of Minimum Bayes-Risk Decoding (2024.naacl-short)
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| Challenge: | Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation. |
| Approach: | They propose to use anomaly detection to measure the degree of approximation by sampling texts from a model and selecting the text with the highest similarity to the others. |
| Outcome: | The proposed method shows that previous hypotheses about samples do not correlate well with the variation, but the results support the core assumption of MBR decoding. |
Penalty Decoding: Well Suppress the Self-Reinforcement Effect in Open-Ended Text Generation (2023.emnlp-main)
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| Challenge: | Experimental results demonstrate the efficacy of our approach in generating high-quality sentences resembling human output. |
| Approach: | They propose a forgetting mechanism that disregards distant tokens, reducing the burden of penalty selection. |
| Outcome: | The proposed approach generates high-quality sentences resembling human output. |
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation (2025.emnlp-industry)
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Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S. Yu
| Challenge: | Existing algorithms for collaborative filtering are limited by their computational demands and latency. |
| Approach: | They propose a framework to integrate pre-trained LLM embeddings into CF models through selective initialization strategies. |
| Outcome: | The proposed framework improves recommendation performance while maintaining low computational costs. |
OASIS: Online Sample Selection for Continual Instruction Tuning (2026.acl-long)
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| Challenge: | Existing methods for continual instruction tuning (CIT) use pre-trained reference models, which are impractical in CIT setups since future data are unknown. |
| Approach: | They propose an adaptive online sample selection approach that estimates each sample’s informativeness relative to all previously seen data and minimizes informative redundancy through iterative selection score updates. |
| Outcome: | Experiments on various large foundation models show that using only 25% of the data achieves comparable performance to full-data training and outperforms the state-of-the-art sampling methods. |
Automatic Extraction of Language-Specific Biomarkers of Healthy Aging in Icelandic (2024.lrec-main)
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| Challenge: | Multiple studies have shown that individuals suffering from AD exhibit difficulties with word retrieval, produce fewer information units and content words, and use more pronouns than healthy age-matched controls. |
| Approach: | They administered three language tasks to participants aged 60–80 to examine the effects of task type and healthy aging on various automatically extracted part-of-speech features in Icelandic. |
| Outcome: | The results show that task type and healthy aging influence language production in Icelandic. |
Batch IS NOT Heavy: Learning Word Representations From All Samples (P18-1)
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| Challenge: | Stochastic Gradient Descent with negative sampling is the most prevalent approach to learn word representations. |
| Approach: | They propose a method that uses batch gradient learning to generate word representations from all training samples. |
| Outcome: | The proposed method outperforms sampling-based methods on several benchmark tasks. |
Follow the leader(board) with confidence: Estimating p-values from a single test set with item and response variance (2023.findings-acl)
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| Challenge: | Among the problems with leaderboard culture in NLP has been the widespread lack of confidence estimation in reported results. |
| Approach: | They propose a framework and simulator for estimating p-values for comparisons between the results of two systems using variance found naturally (though rarely reported) in test set items and individual labels on an item (responses). |
| Outcome: | The proposed framework and simulator are used to estimate p-values for comparisons between the results of two systems under the assumption that the null hypothesis is true. |
The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism (2025.naacl-long)
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| Challenge: | Current evaluations of large language models (LLMs) focus on a single output per example, which limits our understanding of LLM performance variability in real-world applications. |
| Approach: | They explore the performance differences between greedy decoding and sampling and identify benchmarks’ consistency regarding non-determinism and examine unique model behaviors. |
| Outcome: | The proposed model outperforms sampling methods and greedy decoding outperformed other models. |
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding (2023.findings-acl)
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| Challenge: | Existing text decoding methods struggle to produce high-quality text . Greedy and beam search suffer from text degeneration and linguistic diversity issues . |
| Approach: | They propose a family of decoding methods based on minimum bayes risk minimization to address diversity-quality trade-offs in open-ended natural-language generation. |
| Outcome: | The proposed methods improve diversity-quality trade-offs on open-ended natural-language generation tasks. |
Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation (2025.naacl-long)
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| Challenge: | Consistency in the output of language models can vary significantly in style, factual accuracy, and tone, even for similar inputs. |
| Approach: | They propose a decoding algorithm that enhances response consistency across different prompts with no degradation in response quality. |
| Outcome: | The proposed method outperforms standard sampling methods by 10% across semantic and stylistic consistency benchmarks. |
Beyond the Mode: Sequence-Level Distillation of Multilingual Translation Models for Low-Resource Language Pairs (2025.findings-naacl)
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Aarón Galiano-Jiménez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena
| Challenge: | Existing multilingual pre-trained models for low-resource languages have outperformed those trained from scratch for low resources due to high hardware requirements. |
| Approach: | They propose to use beam search to decode the whole output distribution of the teacher to improve student learning. |
| Outcome: | The proposed methods improve student model performance and reduce gender bias amplification common to beam search based methods. |
Distribution Aware Metrics for Conditional Natural Language Generation (2024.lrec-main)
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| Challenge: | Existing metrics for conditional natural language generation rely on pairwise comparisons between a single generated text and the best-matching reference. |
| Approach: | They propose a family of meta-metrics that build on existing pairwise distance functions to evaluate conditional natural language generation models. |
| Outcome: | The proposed method evaluates the ability of a model to generate text matching diversity in references in visual description and summarization. |
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. |
Much Ado About Nothing – Identification of Zero Copulas in Hungarian Using an NMT Model (2020.lrec-1)
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| Challenge: | Zero copulas are the phenomenon that nominal predicates lack an explicit verbal copule in default present tense 3rd person indicative cases. |
| Approach: | They propose a tool that can identify and mark the location of zero copulas in Hungarian clauses that contain nominal predicates at the right position. |
| Outcome: | The proposed tool can identify and mark the location of zero copulas, i.e. where an overt copulan would appear in the non-default cases. |
Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation (2023.findings-emnlp)
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| Challenge: | Recent advances in machine translation (MT) have shown that minimum bayes risk decoding can be a powerful alternative to beam search. |
| Approach: | They propose to use epsilon-sampling to prune away all tokens with a smaller probability mass. |
| Outcome: | The proposed method outperforms beam search decoding and other methods in four languages. |