--- title: "Natural language processing — papers, page 26" description: "Natural language processing: 2421 papers of the Elamur Research library · page 26 of 97 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/nlp/page/26/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/nlp/page/26/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) / page 26 # 💬 Natural language processing 2421 papers · page 26 of 97 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Computational linguistics from the ACL Anthology and arXiv: language models, information extraction, dialogue, evaluation. [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/25/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [25](https://md.research.elamur.ai/free/en/shelves/nlp/page/25/) **26** [27](https://md.research.elamur.ai/free/en/shelves/nlp/page/27/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/27/) - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://md.research.elamur.ai/free/en/papers/lu-2022-fantastically-ordered-prompts-where-find/) — Yao Lu, Max Bartolo, Alastair Moore, et al. · 2022 - [FeTaQA: Free-form Table Question Answering](https://md.research.elamur.ai/free/en/papers/nan-2022-fetaqa-free-form-table-question/) — Linyong Nan, Chiachun Hsieh, Ziming Mao, et al. · 2022 - [Few-Shot Self-Rationalization with Natural Language Prompts](https://md.research.elamur.ai/free/en/papers/marasovic-2022-few-shot-self-rationalization-natural/) — Ana Marasovic, Iz Beltagy, Doug Downey, et al. · 2022 - [FLUTE: Figurative Language Understanding through Textual Explanations](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/) — Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al. · 2022 - [FNet: Mixing Tokens with Fourier Transforms](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/) — James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al. · 2022 - [French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English](https://md.research.elamur.ai/free/en/papers/neveol-2022-french-crows-pairs-extending-challenge/) — Aurélie Névéol, Yoann Dupont, Julien Bezançon, et al. · 2022 - [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/) — Zhengxiao Du, Yujie Qian, Xiao Liu, et al. · 2022 - [GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://md.research.elamur.ai/free/en/papers/wang-2022-gpl-generative-pseudo-labeling-unsupervised/) — Kexin Wang, Nandan Thakur, Nils Reimers, et al. · 2022 - [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/) — Sidney Black, Stella Biderman, Eric Hallahan, et al. · 2022 - [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/) — Meng Cao, Yue Dong, Jackie Cheung · 2022 - [Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew](https://md.research.elamur.ai/free/en/papers/aclbulk-2022-swanson-handling-stress-finite-state-morphological/) — Daniel G. Swanson, Francis M. Tyers · 2022 - [High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics](https://md.research.elamur.ai/free/en/papers/freitag-2022-high-quality-rather-than-high/) — Markus Freitag, David Grangier, Qijun Tan, et al. · 2022 - [How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study](https://md.research.elamur.ai/free/en/papers/cl-2022-esmail-how-much-does-lookahead-matter/) — Saeed Esmail, Kfir Bar, Nachum Dershowitz · 2022 - [Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) — Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al. · 2022 - [IMPLI: Investigating NLI Models' Performance on Figurative Language](https://md.research.elamur.ai/free/en/papers/stowe-2022-impli-investigating-nli-models-performance/) — Kevin Stowe, Prasetya Utama, Iryna Gurevych · 2022 - [Improving Passage Retrieval with Zero-Shot Question Generation](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/) — Devendra Sachan, Mike Lewis, Mandar Joshi, et al. · 2022 - [In-Context Learning for Few-Shot Dialogue State Tracking](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/) — Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, et al. · 2022 - [Internet-Augmented Dialogue Generation](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/) — Mojtaba Komeili, Kurt Shuster, Jason Weston · 2022 - [Investigating Reasons for Disagreement in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/jiang-2022-investigating-reasons-disagreement-natural-language/) — Nan-Jiang Jiang, Marie-Catherine de Marneffe · 2022 - [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://md.research.elamur.ai/free/en/papers/yao-2022-it-is-ai-s-turn/) — Bingsheng Yao, Dakuo Wang, Tongshuang Wu, et al. · 2022 - [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://md.research.elamur.ai/free/en/papers/hu-2022-knowledgeable-prompt-tuning-incorporating-knowledge/) — Shengding Hu, Ning Ding, Huadong Wang, et al. · 2022 - [Large language models are few-shot clinical information extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/) — Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al. · 2022 - [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/) — Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, et al. · 2022 - [LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/) — Michihiro Yasunaga, Jure Leskovec, Percy Liang · 2022 - [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/) — Mandy Guo, Joshua Ainslie, David Uthus, et al. · 2022 [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/25/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [25](https://md.research.elamur.ai/free/en/shelves/nlp/page/25/) **26** [27](https://md.research.elamur.ai/free/en/shelves/nlp/page/27/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/27/)