--- title: "Papers of 2023 — page 4" description: "Papers of 2023: 769 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/2023/page/4/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2023/page/4/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2023 # Papers of 2023 — page 4 769 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [← prev](https://md.research.elamur.ai/free/en/papers/2023/page/3/) [1](https://md.research.elamur.ai/free/en/papers/2023/) [2](https://md.research.elamur.ai/free/en/papers/2023/page/2/) [3](https://md.research.elamur.ai/free/en/papers/2023/page/3/) **4** [5](https://md.research.elamur.ai/free/en/papers/2023/page/5/) … [8](https://md.research.elamur.ai/free/en/papers/2023/page/8/) [next →](https://md.research.elamur.ai/free/en/papers/2023/page/5/) - [HeRo: RoBERTa and Longformer Hebrew Language Models](https://md.research.elamur.ai/free/en/papers/langstream-2023-shalumov-hero-roberta-longformer-hebrew-language/) — Vitaly Shalumov, Harel Haskey · Modern Hebrew - [Hexatagging: Projective Dependency Parsing as Tagging](https://md.research.elamur.ai/free/en/papers/acl-2023-amini-hexatagging-projective-dependency-parsing-as/) — Afra Amini, Tianyu Liu, Ryan Cotterell · English - [Hierarchical Fusion for Online Multimodal Dialog Act Classification](https://md.research.elamur.ai/free/en/papers/miah-2023-hierarchical-fusion-online-multimodal-dialog/) — Md Messal Monem Miah, Adarsh Pyarelal, Ruihong Huang · Findings of the Association for Computational… - [How Are Idioms Processed Inside Transformer Language Models?](https://md.research.elamur.ai/free/en/papers/tian-2023-how-are-idioms-processed-inside/) — Ye Tian, Isobel James, Hye Son · Proceedings of the 12th Joint Conference on… - [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://md.research.elamur.ai/free/en/papers/guo-2023-close-chatgpt-human-experts-comparison/) — Biyang Guo, Xin Zhang, Ziyuan Wang, et al. · arXiv:2301.07597 - [How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) — Michael Hanna, Ollie Liu, Alexandre Variengien · arXiv:2305.00586 - [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/) — Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al. · arXiv:2306.04751 - [How Language Model Hallucinations Can Snowball](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/) — Muru Zhang, Ofir Press, William Merrill, et al. · arXiv:2305.13534 - [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://md.research.elamur.ai/free/en/papers/wang-2023-huatuo-tuning-llama-model-chinese/) — Haochun Wang, Chi Liu, Nuwa Xi, et al. · arXiv:2304.06975 - [HuatuoGPT, Towards Taming Language Model to Be a Doctor](https://md.research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/) — Hongbo Zhang, Junying Chen, Feng Jiang, et al. · Findings of the Association for Computational… - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://md.research.elamur.ai/free/en/papers/shen-2023-hugginggpt-solving-ai-tasks-chatgpt/) — Yongliang Shen, Kaitao Song, Xu Tan, et al. · arXiv:2303.17580 - [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://md.research.elamur.ai/free/en/papers/zeng-2023-iekg-commonsense-knowledge-graph-idiomatic/) — Ziheng Zeng, Kellen Tan Cheng, Srihari Venkat Nanniyur, et al. · Proceedings of the 2023 Conference on Empirical… - [IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/) — Wenhao Yu, Meng Jiang, Peter Clark, et al. · English - [ImageBind: One Embedding Space To Bind Them All](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/) — Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, et al. · arXiv:2305.05665 - [Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing](https://md.research.elamur.ai/free/en/papers/jung-2023-impossible-distillation-low-quality-model/) — Jaehun Jung, Peter West, Liwei Jiang, et al. · arXiv:2305.16635 - [Improved Baselines with Visual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/) — Haotian Liu, Chunyuan Li, Yuheng Li, et al. · arXiv:2310.03744 - [Improving Cross-lingual Transfer through Subtree-aware Word Reordering](https://md.research.elamur.ai/free/en/papers/arviv-2023-improving-cross-lingual-transfer-through/) — Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, et al. · Findings of the Association for Computational… - [Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition](https://md.research.elamur.ai/free/en/papers/fan-2023-improving-dialogue-discourse-parsing-reply/) — Yaxin Fan, Feng Jiang, Peifeng Li, et al. · Proceedings of the 2023 Conference on Empirical… - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/) — Yilun Du, Shuang Li, Antonio Torralba, et al. · arXiv:2305.14325 - [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://md.research.elamur.ai/free/en/papers/fu-2023-improving-language-model-negotiation-self/) — Yao Fu, Hao Peng, Tushar Khot, et al. · arXiv:2305.10142 - [Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training](https://md.research.elamur.ai/free/en/papers/deen-2023-improving-natural-language-inference-arabic/) — Mohammad Majd Saad Al Deen, Maren Pielka, Jorn Hees, et al. · arXiv:2307.14666 - [Improving Self-Confidence, Enthusiasm, And Learning Satisfaction With mE-Book Practical Arabic Syntax and ARIAS Learning Model](https://md.research.elamur.ai/free/en/papers/anisatin-2023-improving-self-confidence-enthusiasm-learning/) — Luthfi Anisatin, Muhammad Khusairi, Nurhidayati Nurhidayati, et al. · Izdihar 6 - [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/) — Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al. · Transactions of the Association for… - [In-context Examples Selection for Machine Translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/) — Sweta Agrawal, Chunting Zhou, Mike Lewis, et al. · Findings of the Association for Computational… - [In-Context Learning Creates Task Vectors](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/) — Roee Hendel, Mor Geva, Amir Globerson · Findings of the Association for Computational… - [IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/) — Jay Gala, Pranjal A. Chitale, Raghavan AK, et al. · arXiv:2305.16307 - [Inference-Time Intervention: Eliciting Truthful Answers from a Language Model](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/) — Kenneth Li, Oam Patel, Fernanda Viegas, et al. · arXiv:2306.03341 - [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhao-2023-infusing-hierarchical-guidance-into-prompt/) — Haodong Zhao, Ruifang He, Mengnan Xiao, et al. · Proceedings of the 61st Annual Meeting of the… - [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://md.research.elamur.ai/free/en/papers/dai-2023-instructblip-towards-general-purpose-vision/) — Wenliang Dai, Junnan Li, Dongxu Li, et al. · arXiv:2305.06500 - [Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) — Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al. · arXiv:2308.10792 - [Instruction Tuning with GPT-4](https://md.research.elamur.ai/free/en/papers/peng-2023-instruction-tuning-gpt-4/) — Baolin Peng, Chunyuan Li, Pengcheng He, et al. · arXiv:2304.03277 - [Instruction-Following Evaluation for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/) — Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, et al. · arXiv:2311.07911 - [INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback](https://md.research.elamur.ai/free/en/papers/xu-2023-instructscore-explainable-text-generation-evaluation/) — Wenda Xu, Danqing Wang, Liangming Pan, et al. · Proceedings of the 2023 Conference on Empirical… - [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/) — Xiao Wang, Weikang Zhou, Can Zu, et al. · arXiv:2304.08085 - [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/) — Zhe Chen, Jiannan Wu, Wenhai Wang, et al. · arXiv:2312.14238 - [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://md.research.elamur.ai/free/en/papers/lex-2023-giulianelli-definition-generation-senses/) — Mario Giulianelli, Iris Luden, Raquel Fernandez, et al. · English - [Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew](https://md.research.elamur.ai/free/en/papers/langstream-2023-shmidman-introducing-dictalm-large-generative-language/) — Shaltiel Shmidman, Avi Shmidman, Amir David Nissan Cohen, et al. · Modern Hebrew - [Investigating Explicitation of Discourse Connectives in Translation using Automatic Annotations](https://md.research.elamur.ai/free/en/papers/yung-2023-investigating-explicitation-discourse-connectives-translation/) — Frances Yung, Merel Scholman, Ekaterina Lapshinova-Koltunski, et al. · Proceedings of the 24th Annual Meeting of the… - [Investigating Multilingual Coreference Resolution by Universal Annotations](https://md.research.elamur.ai/free/en/papers/chai-2023-investigating-multilingual-coreference-resolution-universal/) — Haixia Chai, Michael Strube · Findings of the Association for Computational… - [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/) — Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al. · arXiv:2302.06476 - [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/) — Jiaan Wang, Yunlong Liang, Fandong Meng, et al. · arXiv:2303.04048 - [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://md.research.elamur.ai/free/en/papers/jiao-2023-chatgpt-good-translator-yes-gpt/) — Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, et al. · arXiv:2301.08745 - [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://md.research.elamur.ai/free/en/papers/sun-2023-chatgpt-good-search-investigating-large/) — Weiwei Sun, Lingyong Yan, Xinyu Ma, et al. · arXiv:2304.09542 - [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/liu-2023-your-code-generated-chatgpt-really/) — Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, et al. · arXiv:2305.01210 - [ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development](https://md.research.elamur.ai/free/en/papers/langstream-2023-marmor-ivrit-ai-comprehensive-dataset-hebrew/) — Yanir Marmor, Kinneret Misgav, Yair Lifshitz · Modern Hebrew - [Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbreak-guard-aligned-language-models/) — Zeming Wei, Yifei Wang, Ang Li, et al. · arXiv:2310.06387 - [Jailbreaking Black Box Large Language Models in Twenty Queries](https://md.research.elamur.ai/free/en/papers/chao-2023-jailbreaking-black-box-large-language/) — Patrick Chao, Alexander Robey, Edgar Dobriban, et al. · arXiv:2310.08419 - [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://md.research.elamur.ai/free/en/papers/liu-2023-jailbreaking-chatgpt-prompt-engineering-empirical/) — Yi Liu, Gelei Deng, Zhengzi Xu, et al. · arXiv:2305.13860 - [Jailbroken: How Does LLM Safety Training Fail?](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/) — Alexander Wei, Nika Haghtalab, Jacob Steinhardt · arXiv:2307.02483 - [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/) — Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al. · arXiv:2308.16149 - [Josef Tropper and Rebecca Hasselbach-Andee, Classical Ethiopic: A Grammar of Geˁez, Including Sample Texts and a Glossary](https://md.research.elamur.ai/free/en/papers/priess-2023-josef-tropper-rebecca-hasselbach-andee/) — Maija Priess · Aethiopica 25 - [JudgeLM: Fine-tuned Large Language Models are Scalable Judges](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/) — Lianghui Zhu, Xinggang Wang, Xinlong Wang · arXiv:2310.17631 - [Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction](https://md.research.elamur.ai/free/en/papers/min-2023-just-like-human-would-direct/) — Changrong Min, Ximing Li, Liang Yang, et al. · Proceedings of the 61st Annual Meeting of the… - [Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/) — Jinheon Baek, Alham Fikri Aji, Amir Saffari · arXiv:2306.04136 - [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://md.research.elamur.ai/free/en/papers/peng-2023-kosmos-2-grounding-multimodal-large/) — Zhiliang Peng, Wenhui Wang, Li Dong, et al. · arXiv:2306.14824 - [L-Eval: Instituting Standardized Evaluation for Long Context Language Models](https://md.research.elamur.ai/free/en/papers/an-2023-l-eval-instituting-standardized-evaluation/) — Chenxin An, Shansan Gong, Ming Zhong, et al. · arXiv:2307.11088 - [Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning](https://md.research.elamur.ai/free/en/papers/wang-2023-label-words-anchors-information-flow/) — Lean Wang, Lei Li, Damai Dai, et al. · arXiv:2305.14160 - [Language Is Not All You Need: Aligning Perception with Language Models](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/) — Shaohan Huang, Li Dong, Wenhui Wang, et al. · arXiv:2302.14045 - [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/) — Le Yu, Bowen Yu, Haiyang Yu, et al. · arXiv:2311.03099 - [Language Models can Solve Computer Tasks](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/) — Geunwoo Kim, Pierre Baldi, Stephen McAleer · arXiv:2303.17491 - [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) — Miles Turpin, Julian Michael, Ethan Perez, et al. · arXiv:2305.04388 - [Large Language Model Alignment: A Survey](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/) — Tianhao Shen, Renren Jin, Yufei Huang, et al. · arXiv:2309.15025 - [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/) — Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al. · arXiv:2306.15895 · NeurIPS 2023 - [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/) — Yubo Ma, Yixin Cao, Yong Hong, et al. · Findings of the Association for Computational… - [Large Language Model Unlearning](https://md.research.elamur.ai/free/en/papers/yao-2023-large-language-model-unlearning/) — Yuanshun Yao, Xiaojun Xu, Yang Liu · arXiv:2310.10683 - [Large Language Models are Better Reasoners with Self-Verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/) — Yixuan Weng, Minjun Zhu, Fei Xia, et al. · Findings of the Association for Computational… - [Large Language Models are few(1)-shot Table Reasoners](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/) — Wenhu Chen · Findings of the Association for Computational… - [Large Language Models Are Reasoning Teachers](https://md.research.elamur.ai/free/en/papers/ho-2023-large-language-models-are-reasoning/) — Namgyu Ho, Laura Schmid, Se-Young Yun · Proceedings of the 61st Annual Meeting of the… - [Large Language Models as Optimizers](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/) — Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al. · arXiv:2309.03409 - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/) — Freda Shi, Xinyun Chen, Kanishka Misra, et al. · arXiv:2302.00093 - [Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) — Jiaxin Huang, Shixiang Gu, Le Hou, et al. · Proceedings of the 2023 Conference on Empirical… - [Large Language Models Cannot Self-Correct Reasoning Yet](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/) — Jie Huang, Xinyun Chen, Swaroop Mishra, et al. · arXiv:2310.01798 - [Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist](https://md.research.elamur.ai/free/en/papers/karpinska-2023-large-language-models-effectively-leverage/) — Marzena Karpinska, Mohit Iyyer · Proceedings of the Eighth Conference on Machine… - [Large Language Models for Information Retrieval: A Survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/) — Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen · arXiv:2308.07107 - [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/) — Pouya Pezeshkpour, Estevam Hruschka · arXiv:2308.11483 - [Large Language Models with Controllable Working Memory](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-controllable-working/) — Daliang Li, Ankit Singh Rawat, Manzil Zaheer, et al. · Findings of the Association for Computational… - [Larger language models do in-context learning differently](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/) — Jerry Wei, Jason Wei, Yi Tay, et al. · arXiv:2303.03846 - [LawBench: Benchmarking Legal Knowledge of Large Language Models](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/) — Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al. · arXiv:2309.16289 - [Lawyer LLaMA Technical Report](https://md.research.elamur.ai/free/en/papers/huang-2023-lawyer-llama-technical-report/) — Quzhe Huang, Mingxu Tao, Chen Zhang, et al. · arXiv:2305.15062 - [LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/) — Kaiyu Yang, Aidan M. Swope, Alex Gu, et al. · arXiv:2306.15626 - [Learning to Compress Prompts with Gist Tokens](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/) — Jesse Mu, Xiang Lisa Li, Noah Goodman · arXiv:2304.08467 - [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/) — Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li · arXiv:2308.11462 - [LENS: A Learnable Evaluation Metric for Text Simplification](https://md.research.elamur.ai/free/en/papers/maddela-2023-lens-learnable-evaluation-metric-text/) — Mounica Maddela, Yao Dou, David Heineman, et al. · Proceedings of the 61st Annual Meeting of the… - [Let's Verify Step by Step](https://md.research.elamur.ai/free/en/papers/lightman-2023-let-s-verify-step-step/) — Hunter Lightman, Vineet Kosaraju, Yura Burda, et al. · arXiv:2305.20050 - [LEVER: Learning to Verify Language-to-Code Generation with Execution](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/) — Ansong Ni, Srini Iyer, Dragomir Radev, et al. · arXiv:2302.08468 - [Leveraging BERT Language Model for Arabic Long Document Classification](https://md.research.elamur.ai/free/en/papers/al-2023-qurishi-leveraging-bert-language-model-arabic/) — Muhammad AL-Qurishi · arXiv:2305.03519 - [Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition](https://md.research.elamur.ai/free/en/papers/abdallah-2023-leveraging-data-collection-unsupervised-learning/) — Ahmed Amine Ben Abdallah, Ata Kabboudi, Amir Kanoun, et al. · arXiv:2309.11327 - [Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach](https://md.research.elamur.ai/free/en/papers/maziarz-2023-lexicalised-non-lexicalized-multi-word/) — Marek Maziarz, Łukasz Grabowski, Tadeusz Piotrowski, et al. · Proceedings of the 12th Global Wordnet… - [LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain](https://md.research.elamur.ai/free/en/papers/niklaus-2023-lextreme-multi-lingual-multi-task/) — Joel Niklaus, Veton Matoshi, Pooja Rani, et al. · Findings of the Association for Computational… - [LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/) — Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al. · arXiv:2310.15164… - [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/otmazgin-2023-lingmess-linguistically-informed-multi-expert/) — Shon Otmazgin, Arie Cattan, Yoav Goldberg · English - [Linguistic Productivity: the Case of Determiners in English](https://md.research.elamur.ai/free/en/papers/alhama-2023-linguistic-productivity-case-determiners-english/) — Raquel G. 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