--- title: "Papers of 2022 — page 3" description: "Papers of 2022: 455 papers of the Elamur Research library — what each work is about in our own words, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/papers/2022/page/3/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2022/page/3/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2022 # Papers of 2022 — page 3 455 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [← prev](https://md.research.elamur.ai/free/en/papers/2022/page/2/) [1](https://md.research.elamur.ai/free/en/papers/2022/) [2](https://md.research.elamur.ai/free/en/papers/2022/page/2/) **3** [4](https://md.research.elamur.ai/free/en/papers/2022/page/4/) [5](https://md.research.elamur.ai/free/en/papers/2022/page/5/) [next →](https://md.research.elamur.ai/free/en/papers/2022/page/4/) - [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. · Findings of the Association for Computational… - [Infinitivketten in biblischen und qumranischen Texten (Infinitive chains in biblical and Qumran texts)](https://md.research.elamur.ai/free/en/papers/hbo-2022-bindrim-infinitivketten-biblischen-und-qumranischen-texten/) — L. Bindrim · Ancient Hebrew - [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://md.research.elamur.ai/free/en/papers/he-2022-infusing-knowledge-wikipedia-enhance-stance/) — Zihao He, Negar Mokhberian, Kristina Lerman · Proceedings of the 12th Workshop on… - [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://md.research.elamur.ai/free/en/papers/trivedi-2022-interleaving-retrieval-chain-thought-reasoning/) — Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, et al. · arXiv:2212.10509 - [Internet-Augmented Dialogue Generation](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/) — Mojtaba Komeili, Kurt Shuster, Jason Weston · Proceedings of the 60th Annual Meeting of the… - [Internet-augmented language models through few-shot prompting for open-domain question answering](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/) — Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al. · arXiv:2203.05115 - [Interpretability for Language Learners Using Example-Based Grammatical Error Correction](https://md.research.elamur.ai/free/en/papers/kaneko-2022-interpretability-language-learners-example-based/) — Masahiro Kaneko, Sho Takase, Ayana Niwa, et al. · Proceedings of the 60th Annual Meeting of the… - [Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language](https://md.research.elamur.ai/free/en/papers/hbo-2022-shmidman-introducing-berel-bert-embeddings-rabbinic/) — Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, et al. · Ancient Hebrew - [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 · Transactions of the Association for… - [Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences](https://md.research.elamur.ai/free/en/papers/kabbara-2022-investigating-performance-transformer-based-nli/) — Jad Kabbara, Jackie Chi Kit Cheung · Proceedings of the 29th International… - [Is GPT-3 a Good Data Annotator?](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/) — Bosheng Ding, Chengwei Qin, Linlin Liu, et al. · arXiv:2212.10450 - [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://md.research.elamur.ai/free/en/papers/ramamurthy-2022-reinforcement-learning-natural-language-processing/) — Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiante Brantley, et al. · arXiv:2210.01241 - [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. · English - [It's not Rocket Science: Interpreting Figurative Language in Narratives](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-it-s-not-rocket-science/) — Tuhin Chakrabarty, Yejin Choi, Vered Shwartz · Research library - [JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams](https://md.research.elamur.ai/free/en/papers/kerner-2022-jct-semeval-2022-task-6/) — Yaakov HaCohen-Kerner, Matan Fchima, Ilan Meyrowitsch · Proceedings of the 16th International Workshop… - [JGLUE: Japanese General Language Understanding Evaluation](https://md.research.elamur.ai/free/en/papers/kurihara-2022-jglue-japanese-general-language-understanding/) — Kentaro Kurihara, Daisuke Kawahara, Tomohide Shibata · Proceedings of the Thirteenth Language… - [Joint Coreference Resolution for Zeros and non-Zeros in Arabic](https://md.research.elamur.ai/free/en/papers/aloraini-2022-joint-coreference-resolution-zeros-non/) — Abdulrahman Aloraini, Sameer Pradhan, Massimo Poesio · Proceedings of the Seventh Arabic Natural… - [JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection](https://md.research.elamur.ai/free/en/papers/liang-2022-jointcl-joint-contrastive-learning-framework/) — Bin Liang, Qinglin Zhu, Xiang Li, et al. · Proceedings of the 60th Annual Meeting of the… - [Just a Series of Misunderstandings? Assyria and B?t-Zam?ni, ?adi-/I?tadi-libbušu, and Aramaic in the early Neo-Assyrian State](https://md.research.elamur.ai/free/en/papers/edmonds-2022-just-series-misunderstandings-assyria-b/) — Alexander Johannes Edmonds · Asia Anteriore Antica 3 - [Knowledge Unlearning for Mitigating Privacy Risks in Language Models](https://md.research.elamur.ai/free/en/papers/jang-2022-knowledge-unlearning-mitigating-privacy-risks/) — Joel Jang, Dongkeun Yoon, Sohee Yang, et al. · arXiv:2210.01504 - [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. · Proceedings of the 60th Annual Meeting of the… - [Label distributions help implicit discourse relation classification](https://md.research.elamur.ai/free/en/papers/yung-2022-label-distributions-help-implicit-discourse/) — Frances Yung, Kaveri Anuranjana, Merel Scholman, et al. · Proceedings of the 3rd Workshop on… - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/) — Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al. · arXiv:2210.08402 - [LaMDA: Language Models for Dialog Applications](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/) — Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al. · arXiv:2201.08239 - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/) — Abulhair Saparov, He He · arXiv:2210.01240 - [Language Models are Multilingual Chain-of-Thought Reasoners](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/) — Freda Shi, Mirac Suzgun, Markus Freitag, et al. · arXiv:2210.03057 - [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/) — Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al. · arXiv:2201.07207 - [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. · Proceedings of the 2022 Conference on Empirical… - [Large Language Models Are Human-Level Prompt Engineers](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/) — Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al. · arXiv:2211.01910 - [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) — Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al. · Research library - [Large Language Models Encode Clinical Knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/) — Karan Singhal, Shekoofeh Azizi, Tao Tu, et al. · arXiv:2212.13138 - [Large Language Models Struggle to Learn Long-Tail Knowledge](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/) — Nikhil Kandpal, Haikang Deng, Adam Roberts, et al. · arXiv:2211.08411 - [Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All](https://md.research.elamur.ai/free/en/papers/hbo-2022-gueta-large-pre-trained-models-extra/) — Eylon Gueta, Avi Shmidman, Shaltiel Shmidman, et al. · Ancient Hebrew - [Large Scale Substitution-based Word Sense Induction](https://md.research.elamur.ai/free/en/papers/lex-2022-eyal-substitution-wsi/) — Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, et al. · English - [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://md.research.elamur.ai/free/en/papers/huang-2022-layoutlmv3-pre-training-document-ai/) — Yupan Huang, Tengchao Lv, Lei Cui, et al. · arXiv:2204.08387 - [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://md.research.elamur.ai/free/en/papers/jie-2022-learning-reason-deductively-math-word/) — Zhanming Jie, Jierui Li, Wei Lu · Mova's core list - [Least-to-most prompting enables complex reasoning in large language models](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/) — Research library - [Leveraging Large Language Models for Multiple Choice Question Answering](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/) — Joshua Robinson, Christopher Michael Rytting, David Wingate · arXiv:2210.12353 - [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. · Proceedings of the 2022 Conference of the North… - [LILA: A Unified Benchmark for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/) — Swaroop Mishra, Matthew Finlayson, Pan Lu, et al. · Proceedings of the 2022 Conference on Empirical… - [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://md.research.elamur.ai/free/en/papers/ma-2022-linguistic-rules-based-corpus-generation/) — Shirong Ma, Yinghui Li, Rongyi Sun, et al. · Findings of the Association for Computational… - [Linguistically Motivated Evaluation of the 2022 State-of-the-art Machine Translation Systems for Three Language Directions](https://md.research.elamur.ai/free/en/papers/macketanz-2022-linguistically-motivated-evaluation-2022-state/) — Vivien Macketanz, Shushen Manakhimova, Eleftherios Avramidis, et al. · Machine translation - [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 · Proceedings of the 60th Annual Meeting of the… - [Lip̄nē 'in the face of': A Locative Preposition with a Threatening Connotation](https://md.research.elamur.ai/free/en/papers/hbo-2022-staps-lipne-face-locative-preposition-threatening/) — Camil Staps · Ancient Hebrew - [Lisan: Yemeni, Iraqi, Libyan, and Sudanese Arabic Dialect Copora with Morphological Annotations](https://md.research.elamur.ai/free/en/papers/jarrar-2022-lisan-yemeni-iraqi-libyan-sudanese/) — Mustafa Jarrar, Fadi A Zaraket, Tymaa Hammouda, et al. · arXiv:2212.06468 - [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2022-llm-planner-few-shot-grounded/) — Chan Hee Song, Jiaman Wu, Clayton Washington, et al. · arXiv:2212.04088 - [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. · Findings of the Association for Computational… - [Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?](https://md.research.elamur.ai/free/en/papers/patel-2022-low-resource-authorship-style-transfer/) — Ajay Patel, Nicholas Andrews, Chris Callison-Burch · Machine translation - [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://md.research.elamur.ai/free/en/papers/jung-2022-maieutic-prompting-logically-consistent-reasoning/) — Jaehun Jung, Lianhui Qin, Sean Welleck, et al. · Proceedings of the 2022 Conference on Empirical… - [Making Italian Parliamentary Records Machine-Actionable: the Construction of the ParlaMint-IT corpus](https://md.research.elamur.ai/free/en/papers/agnoloni-2022-making-italian-parliamentary-records-machine/) — Tommaso Agnoloni, Roberto Bartolini, Francesca Frontini, et al. · Proceedings of the Workshop ParlaCLARIN III… - [MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners](https://md.research.elamur.ai/free/en/papers/najafi-2022-marsan-semeval-2022-task-6/) — Maryam Najafi, Ehsan Tavan · Proceedings of the 16th International Workshop… - [Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets](https://md.research.elamur.ai/free/en/papers/altaher-2022-masader-plus-new-interface-exploring/) — Yousef Altaher, Ali Fadel, Mazen Alotaibi, et al. · arXiv:2208.00932 - [Masader: Metadata Sourcing for Arabic Text and Speech Data Resources](https://md.research.elamur.ai/free/en/papers/alyafeai-2022-masader-metadata-sourcing-arabic-text/) — Zaid Alyafeai, Maraim Masoud, Mustafa Ghaleb, et al. · Proceedings of the Thirteenth Language… - [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/adelani-2022-masakhaner-2-0-africa-centric/) — David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, et al. · Proceedings of the 2022 Conference on Empirical… - [MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem](https://md.research.elamur.ai/free/en/papers/perrella-2022-matese-machine-translation-evaluation-as/) — Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, et al. · Proceedings of the Seventh Conference on… - [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://md.research.elamur.ai/free/en/papers/wang-2022-maven-ere-unified-large-scale/) — Xiaozhi Wang, Yulin Chen, Ning Ding, et al. · Proceedings of the 2022 Conference on Empirical… - [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://md.research.elamur.ai/free/en/papers/pal-2022-medmcqa-large-scale-multi-subject/) — Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu · arXiv:2203.14371 · ACM Conference on Health… - [Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) — Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al. · arXiv:2205.10770 - [mGPT: Few-Shot Learners Go Multilingual](https://md.research.elamur.ai/free/en/papers/shliazhko-2022-mgpt-few-shot-learners-go/) — Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, et al. · arXiv:2204.07580 - [MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset](https://md.research.elamur.ai/free/en/papers/fomicheva-2022-mlqe-pe-multilingual-quality-estimation/) — Marina Fomicheva, Shuo Sun, Erick Fonseca, et al. · Proceedings of the Thirteenth Language… - [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/) — Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al. · arXiv:2203.05482 - [Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction](https://md.research.elamur.ai/free/en/papers/agarwal-2022-model-transfer-event-tracking-as/) — Sumit Agarwal, Rosanna Vitiello, Carolyn Rosé · Proceedings of the First Workshop On Transcript… - [Morphologically annotated corpora of Pomak](https://md.research.elamur.ai/free/en/papers/a-2022-morphologically-annotated-corpora-pomak/) — Ritván Jusúf Karahóǧa, Panagiotis G. Krimpas, Vivian Stamou, et al. · Proceedings of the Fifth Workshop on the Use of… - [Morphotactic Modeling in an Open-source Multi-dialectal Arabic Morphological Analyzer and Generator](https://md.research.elamur.ai/free/en/papers/lingbulk-2022-habash-morphotactic-modeling-open-source-multi/) — Nizar Habash, Reham Marzouk, Christian Khairallah, et al. · Semitic languages - [MTEB: Massive Text Embedding Benchmark](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/) — Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al. · arXiv:2210.07316 - [Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network](https://md.research.elamur.ai/free/en/papers/liang-2022-multi-modal-sarcasm-detection-cross/) — Bin Liang, Chenwei Lou, Xiang Li, et al. · Proceedings of the 60th Annual Meeting of the… - [MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/malmasi-2022-multiconer-large-scale-multilingual-dataset/) — Shervin Malmasi, Anjie Fang, Besnik Fetahu, et al. · Proceedings of the 29th International… - [Multimodal Sarcasm Target Identification in Tweets](https://md.research.elamur.ai/free/en/papers/wang-2022-multimodal-sarcasm-target-identification-tweets/) — Jiquan Wang, Lin Sun, Yi Liu, et al. · Proceedings of the 60th Annual Meeting of the… - [MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation](https://md.research.elamur.ai/free/en/papers/ye-2022-multiwoz-2-4-multi-domain/) — Fanghua Ye, Jarana Manotumruksa, Emine Yilmaz · Proceedings of the 23rd Annual Meeting of the… - [MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases](https://md.research.elamur.ai/free/en/papers/martin-2022-muss-multilingual-unsupervised-sentence-simplification/) — Louis Martin, Angela Fan, Éric de la Clergerie, et al. · Proceedings of the Thirteenth Language… - [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/muhammad-2022-naijasenti-nigerian-twitter-sentiment-corpus/) — Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, et al. · Proceedings of the Thirteenth Language… - [NARC – Norwegian Anaphora Resolution Corpus](https://md.research.elamur.ai/free/en/papers/hlum-2022-narc-norwegian-anaphora-resolution-corpus/) — Petter Mæhlum, Dag Haug, Tollef Jørgensen, et al. · Proceedings of the Fifth Workshop on… - [Negating the infinitive in Biblical Hebrew](https://md.research.elamur.ai/free/en/papers/hbo-2022-ehrensvard-negating-infinitive-biblical-hebrew/) — Martin Ehrensvärd · Ancient Hebrew - [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/) — Ximing Lu, Sean Welleck, Peter West, et al. · Proceedings of the 2022 Conference of the North… - [NTREX-128 – News Test References for MT Evaluation of 128 Languages](https://md.research.elamur.ai/free/en/papers/federmann-2022-ntrex-128-news-test-references/) — Christian Federmann, Tom Kocmi, Ying Xin · Proceedings of the First Workshop on Scaling Up… - [Nucleus Composition in Transition-based Dependency Parsing](https://md.research.elamur.ai/free/en/papers/ud-2022-nivre-nucleus-composition-transition-based-dependency/) — Joakim Nivre, Ali Basirat, Luise Dürlich, et al. · English - [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/) — Peng Wang, An Yang, Rui Men, et al. · arXiv:2202.03052 - [Offensive language detection in Hebrew: can other languages help?](https://md.research.elamur.ai/free/en/papers/lingbulk-2022-litvak-offensive-language-detection-hebrew-can/) — Marina Litvak, Natalia Vanetik, Chaya Liebeskind, et al. · Modern Hebrew - [Offensive Language Detection in Under-resourced Algerian Dialectal Arabic Language](https://md.research.elamur.ai/free/en/papers/boucherit-2022-offensive-language-detection-under-resourced/) — Oussama Boucherit, Kheireddine Abainia · arXiv:2203.10024 - [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/) — Nouha Dziri, Sivan Milton, Mo Yu, et al. · Proceedings of the 2022 Conference of the North… - [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/) — Hongjin Su, Weijia Shi, Jungo Kasai, et al. · arXiv:2212.09741 - [OpenPrompt: An Open-source Framework for Prompt-learning](https://md.research.elamur.ai/free/en/papers/ding-2022-openprompt-open-source-framework-prompt/) — Ning Ding, Shengding Hu, Weilin Zhao, et al. · Proceedings of the 60th Annual Meeting of the… - [OpenStance: Real-world Zero-shot Stance Detection](https://md.research.elamur.ai/free/en/papers/xu-2022-openstance-real-world-zero-shot/) — Hanzi Xu, Slobodan Vucetic, Wenpeng Yin · Proceedings of the 26th Conference on… - [Opinion Piece: Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes](https://md.research.elamur.ai/free/en/papers/zeldes-2022-opinion-piece-can-we-fix/) — Amir Zeldes · English - [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/) — Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al. · arXiv:2212.12017 - [Out of One, Many: Using Language Models to Simulate Human Samples](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/) — Lisa P. Argyle, Ethan C. 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