--- title: "Natural language processing — papers, page 18" description: "Natural language processing: 2421 papers of the Elamur Research library · page 18 of 97 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/nlp/page/18/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/nlp/page/18/) [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 18 # 💬 Natural language processing 2421 papers · page 18 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/17/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [17](https://md.research.elamur.ai/free/en/shelves/nlp/page/17/) **18** [19](https://md.research.elamur.ai/free/en/shelves/nlp/page/19/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/19/) - [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/) — Jinyan Su, Terry Zhuo, Di Wang, et al. · 2023 - [DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://md.research.elamur.ai/free/en/papers/arxivrecent-2023-shmidman-dictabert-state-art-bert-suite/) — Shaltiel Shmidman, Avi Shmidman, Moshe Koppel · 2023 - [Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://md.research.elamur.ai/free/en/papers/ko-2023-discourse-analysis-questions-answers-parsing/) — Wei-Jen Ko, Yating Wu, Cutter Dalton, et al. · 2023 - [Diverse Demonstrations Improve In-context Compositional Generalization](https://md.research.elamur.ai/free/en/papers/levy-2023-diverse-demonstrations-improve-context-compositional/) — Itay Levy, Ben Bogin, Jonathan Berant · 2023 - [Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/) — Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al. · 2023 - [Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?](https://md.research.elamur.ai/free/en/papers/eacl-2023-shmidman-do-pretrained-contextual-language-models/) — Avi Shmidman, Cheyn Shmuel Shmidman, Dan Bareket, et al. · 2023 - [Document-Level Machine Translation with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-document-level-machine-translation-large/) — Longyue Wang, Chenyang Lyu, Tianbo Ji, et al. · 2023 - [DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/valipour-2023-dylora-parameter-efficient-tuning-pre/) — Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, et al. · 2023 - [Elaborative Simplification as Implicit Questions Under Discussion](https://md.research.elamur.ai/free/en/papers/wu-2023-elaborative-simplification-as-implicit-questions/) — Yating Wu, William Sheffield, Kyle Mahowald, et al. · 2023 - [Enabling Large Language Models to Generate Text with Citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/) — Tianyu Gao, Howard Yen, Jiatong Yu, et al. · 2023 - [Encoding Discourse Structure: Comparison of RST and QUD](https://md.research.elamur.ai/free/en/papers/shahmohammadi-2023-encoding-discourse-structure-comparison-rst/) — Sara Shahmohammadi, Hannah Seemann, Manfred Stede, et al. · 2023 - [Evaluating Object Hallucination in Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/) — Yifan Li, Yifan Du, Kun Zhou, et al. · 2023 - [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/) — Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al. · 2023 - [Evaluating statistical language models as pragmatic reasoners](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/) — Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al. · 2023 - [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://md.research.elamur.ai/free/en/papers/zhang-2023-evaluating-performance-large-language/) — Xiaotian Zhang, Chunyang Li, Yi Zong, et al. · 2023 - [Evaluating Verifiability in Generative Search Engines](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/) — Nelson Liu, Tianyi Zhang, Percy Liang · 2023 - [Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities](https://md.research.elamur.ai/free/en/papers/orlando-2023-exploring-non-verbal-predicates-semantic/) — Riccardo Orlando, Simone Conia, Roberto Navigli · 2023 - [Fact-Checking Complex Claims with Program-Guided Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/) — Liangming Pan, Xiaobao Wu, Xinyuan Lu, et al. · 2023 - [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/) — Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al. · 2023 - [Factuality Challenges in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/) — Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, et al. · 2023 - [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/) — Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al. · 2023 - [Fine-tuning Language Models for Factuality](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/) — Katherine Tian, Eric Mitchell, Huaxiu Yao, et al. · 2023 - [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://md.research.elamur.ai/free/en/papers/riley-2023-frmt-benchmark-few-shot-region/) — Parker Riley, Timothy Dozat, Jan A. Botha, et al. · 2023 - [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/) — Shangbin Feng, Chan Young Park, Yuhan Liu, et al. · 2023 - [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://md.research.elamur.ai/free/en/papers/samsi-2023-words-watts-benchmarking-energy-costs/) — Siddharth Samsi, Dan Zhao, Joseph McDonald, et al. · 2023 [← prev](https://md.research.elamur.ai/free/en/shelves/nlp/page/17/) [1](https://md.research.elamur.ai/free/en/shelves/nlp/) … [17](https://md.research.elamur.ai/free/en/shelves/nlp/page/17/) **18** [19](https://md.research.elamur.ai/free/en/shelves/nlp/page/19/) … [97](https://md.research.elamur.ai/free/en/shelves/nlp/page/97/) [next →](https://md.research.elamur.ai/free/en/shelves/nlp/page/19/)