The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future | Paper Citation Record

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This paper cites IEEE Transactions on Software Engineering (2024).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future IEEE Transactions on Software Engineering (2024)

Reference 1

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This paper cites ACM Transactions on Software Engineering and Methodology (2024).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future ACM Transactions on Software Engineering and Methodology (2024)

Reference 2

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This paper cites From LLM to NMT: Advancing Low-Resource Machine Translation with Claude.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future From LLM to NMT: Advancing Low-Resource Machine Translation with Claude

Reference 3

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This paper cites In: Proceedings of the Ninth Conference on Machine Translation, pp.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: Proceedings of the Ninth Conference on Machine Translation, pp

Reference 4

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This paper cites Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping

Reference 5

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This paper cites Plos one17(7), 0270275 (2022).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Plos one17(7), 0270275 (2022)

Reference 6

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This paper cites Complex & Intelligent Systems11(6), 1–22 (2025).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Complex & Intelligent Systems11(6), 1–22 (2025)

Reference 7

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This paper cites PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification

Reference 8

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This paper cites IEEE access 78 12, 26839–26874 (2024).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future IEEE access 78 12, 26839–26874 (2024)

Reference 9

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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This paper cites https://developer.nvidia.com/blog/ using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future https://developer.nvidia.com/blog/ using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/

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This paper cites Artificial Intelligence in Medicine157, 103003 (2024).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Artificial Intelligence in Medicine157, 103003 (2024)

Reference 12

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This paper cites https://www.forbes.com/councils/forbestechcouncil/2024/ 04/26/the-untold-story-of-ais-huge-carbon-footprint/ (2024).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future https://www.forbes.com/councils/forbestechcouncil/2024/ 04/26/the-untold-story-of-ais-huge-carbon-footprint/ (2024)

Reference 13

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This paper cites Unleashing the potential of prompt engineering for large language models.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Unleashing the potential of prompt engineering for large language models

Reference 14

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This paper cites Learning How to Ask: Querying LMs with Mixtures of Soft Prompts.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 15

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This paper cites Advances in Neural Information Processing Systems36, 51008–51025 (2023).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Advances in Neural Information Processing Systems36, 51008–51025 (2023)

Reference 16

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This paper cites Journal of Computer Languages70, 101117 (2022).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Journal of Computer Languages70, 101117 (2022)

Reference 17

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Unresolved cited work

Reference 18

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Observation d240b69a-c507-451c-aec0-61c591292086 · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 19

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This paper cites Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?

Reference 20

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Observation ad22bfc5-2f1f-4ad2-8b02-9cc056568586 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 21

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This paper cites AI Open5, 208–215 (2024).

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future AI Open5, 208–215 (2024)

Reference 22

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This paper cites In: International Conference on Machine Learning, pp.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: International Conference on Machine Learning, pp

Reference 23

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This paper cites FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models

Reference 24

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

Reference 25

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models

Reference 26

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 27

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 28

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future BBTv2: Towards a Gradient-Free Future with Large Language Models

Reference 29

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This paper cites In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Com- putational Linguistics: Human Language Technologies, pp.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Com- putational Linguistics: Human Language Technologies, pp

Reference 30

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 31

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Transactions on Machine Learning Research2023(2023)

Reference 32

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future TEMPERA: Test-Time Prompting via Reinforcement Learning

Reference 33

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Reference 34

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future PRewrite: Prompt Rewriting with Reinforcement Learning

Reference 35

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models

Reference 36

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL

Reference 37

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization

Reference 38

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

Reference 39

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning

Reference 40

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 41

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: 2024 IEEE Congress on Evolutionary Computation (CEC), pp

Reference 43

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Learning To Retrieve Prompts for In-Context Learning

Reference 44

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Active Example Selection for In-Context Learning

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Automatic Chain of Thought Prompting in Large Language Models

Reference 46

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data

Reference 47

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: International Conference on Machine Learning, pp

Reference 48

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars

Reference 50

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This paper cites In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

Reference 51

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 52

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This paper cites Large Language Models as Optimizers.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Large Language Models as Optimizers

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Prompt Engineering a Prompt Engineer

Reference 54

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Strings from the Library of Babel: Random Sampling as a Strong Baseline for Prompt Optimisation

Reference 55

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Black-Box Prompt Optimization: Aligning Large Language Models without Model Training

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Mathematics12(6), 929 (2024)

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Complex & Intelligent Systems8(6), 4663–4678 (2022)

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Advances in neural information processing systems28(2015)

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Semantic web6(2), 167–195 (2015)

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future SQuAD: 100,000+ Questions for Machine Comprehension of Text

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future NewsQA: A Machine Comprehension Dataset

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future https://www.bioasq.org/

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future TruthfulQA: Measuring How Models Mimic Human Falsehoods

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Applied Sciences11(14), 6421 (2021)

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

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The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future arXiv preprint arXiv: 180407461 (2018)

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