--- title: "Papers of 2023 — page 1" description: "Papers of 2023: 635 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/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2023/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2023 # Papers of 2023 — page 1 635 papers · [all years](https://md.research.elamur.ai/free/en/papers/) **1** [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](https://md.research.elamur.ai/free/en/papers/2023/page/4/) [5](https://md.research.elamur.ai/free/en/papers/2023/page/5/) [6](https://md.research.elamur.ai/free/en/papers/2023/page/6/) [7](https://md.research.elamur.ai/free/en/papers/2023/page/7/) - ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://md.research.elamur.ai/free/en/papers/shen-2023-anything-now-characterizing-evaluating-wild/) — Xinyue Shen, Zeyuan Chen, Michael Backes, et al. · arXiv:2308.03825 - ["Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures](https://md.research.elamur.ai/free/en/papers/kim-2023-pope-catholic-applying-chain-thought/) — Zae Myung Kim, David E. Taylor, Dongyeop Kang · English - ["You are Cursed by the God YHW": an early Hebrew inscription from Mt. Ebal](https://md.research.elamur.ai/free/en/papers/hbo-2023-stripling-cursed-god-yhw-early-hebrew/) — Scott Stripling, Gershon Galil, Ivana Kumpová, et al. · Ancient Hebrew - [2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems](https://md.research.elamur.ai/free/en/papers/ahmed-2023-2-n-is-better-than/) — Shafiuddin Rehan Ahmed, Abhijnan Nath, James H. Martin, et al. · Findings of the Association for Computational… - [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) — Humza Naveed, Asad Ullah Khan, Shi Qiu, et al. · arXiv:2307.06435 - [A fine-grained comparison of pragmatic language understanding in humans and language models](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/) — Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al. · English - [A General Theoretical Paradigm to Understand Learning from Human Preferences](https://md.research.elamur.ai/free/en/papers/azar-2023-general-theoretical-paradigm-understand-learning/) — Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, et al. · arXiv:2310.12036 - [A Massive Scale Semantic Similarity Dataset of Historical English](https://md.research.elamur.ai/free/en/papers/silcock-2023-massive-scale-semantic-similarity-dataset/) — Emily Silcock, Melissa Dell · English - [A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis](https://md.research.elamur.ai/free/en/papers/wu-2023-multi-task-dataset-assessing-discourse/) — Hongyi Wu, Xinshu Shen, Man Lan, et al. · Proceedings of the 2023 Conference on Empirical… - [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/) — Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al. · arXiv:2302.04023 - [A New Direction in Stance Detection: Target-Stance Extraction in the Wild](https://md.research.elamur.ai/free/en/papers/li-2023-new-direction-stance-detection-target/) — Yingjie Li, Krishna Garg, Cornelia Caragea · Proceedings of the 61st Annual Meeting of the… - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://md.research.elamur.ai/free/en/papers/white-2023-prompt-pattern-catalog-enhance-prompt/) — Jules White, Quchen Fu, Sam Hays, et al. · arXiv:2302.11382 - [A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/) — Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu · arXiv:2307.03987 - [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://md.research.elamur.ai/free/en/papers/blaschke-2023-survey-corpora-germanic-low-resource/) — Verena Blaschke, Hinrich Schuetze, Barbara Plank · English - [A Survey of Hallucination in Large Foundation Models](https://md.research.elamur.ai/free/en/papers/rawte-2023-survey-hallucination-large-foundation-models/) — Vipula Rawte, Amit Sheth, Amitava Das · arXiv:2309.05922 - [A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) — Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · arXiv:2303.18223 · Frontiers of Computer… - [A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) — Yupeng Chang, Xu Wang, Jindong Wang, et al. · arXiv:2307.03109 - [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) — Lei Huang, Weijiang Yu, Weitao Ma, et al. · arXiv:2311.05232 - [A Survey on In-context Learning](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/) — Qingxiu Dong, Lei Li, Damai Dai, et al. · arXiv:2301.00234 - [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/) — Yifan Yao, Jinhao Duan, Kaidi Xu, et al. · arXiv:2312.02003 - [A Survey on Large Language Model based Autonomous Agents](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-large-language-model-based/) — Lei Wang, Chen Ma, Xueyang Feng, et al. · arXiv:2308.11432 - [A Survey on Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/yin-2023-survey-multimodal-large-language-models/) — Shukang Yin, Chaoyou Fu, Sirui Zhao, et al. · arXiv:2306.13549 - [A Transformer-based parser for Syriac morphology](https://md.research.elamur.ai/free/en/papers/aclbulk-2023-naaijer-transformer-based-parser-syriac-morphology/) — Martijn Naaijer, Constantijn Sikkel, Mathias Coeckelbergs, et al. · Aramaic - [A Watermark for Large Language Models](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-watermark-large-language-models/) — John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al. · arXiv:2301.10226 - [Acquiring Linguistic Knowledge from Multimodal Input](https://md.research.elamur.ai/free/en/papers/amariucai-2023-acquiring-linguistic-knowledge-multimodal-input/) — Theodor Amariucai, Alexander Scott Warstadt · Proceedings of the BabyLM Challenge at the 27th… - [AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/) — Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al. · arXiv:2303.10512 - [Adapting Language Models to Compress Contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/) — Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al. · Proceedings of the 2023 Conference on Empirical… - [Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/) — Jian Xie, Kai Zhang, Jiangjie Chen, et al. · arXiv:2305.13300 - [Adaptive Machine Translation with Large Language Models](https://md.research.elamur.ai/free/en/papers/moslem-2023-adaptive-machine-translation-large-language/) — Yasmin Moslem, Rejwanul Haque, John D. Kelleher, et al. · arXiv:2301.13294 - [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://md.research.elamur.ai/free/en/papers/alhafni-2023-advancements-arabic-grammatical-error-detection/) — Bashar Alhafni, Go Inoue, Christian Khairallah, et al. · Proceedings of the 2023 Conference on Empirical… - [Adversarial Network with External Knowledge for Zero-Shot Stance Detection](https://md.research.elamur.ai/free/en/papers/wang-2023-adversarial-network-external-knowledge-zero/) — Chunling Wang, Yijia Zhang, Xingyu Yu, et al. · Proceedings of the 22nd Chinese National… - [AgentBench: Evaluating LLMs as Agents](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/) — Xiao Liu, Hao Yu, Hanchen Zhang, et al. · arXiv:2308.03688 - [AgentTuning: Enabling Generalized Agent Abilities for LLMs](https://md.research.elamur.ai/free/en/papers/zeng-2023-agenttuning-enabling-generalized-agent/) — Aohan Zeng, Mingdao Liu, Rui Lu, et al. · arXiv:2310.12823 - [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://md.research.elamur.ai/free/en/papers/chen-2023-agentverse-facilitating-multi-agent/) — Weize Chen, Yusheng Su, Jingwei Zuo, et al. · arXiv:2308.10848 - [Aligning Large Language Models with Human: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/) — Yufei Wang, Wanjun Zhong, Liangyou Li, et al. · arXiv:2307.12966 - [Aligning Large Multimodal Models with Factually Augmented RLHF](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/) — Zhiqing Sun, Sheng Shen, Shengcao Cao, et al. · arXiv:2309.14525 - [AlignScore: Evaluating Factual Consistency with A Unified Alignment Function](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/) — Yuheng Zha, Yichi Yang, Ruichen Li, et al. · Proceedings of the 61st Annual Meeting of the… - [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/) — Yann Dubois, Xuechen Li, Rohan Taori, et al. · arXiv:2305.14387 - [AlpaGasus: Training A Better Alpaca with Fewer Data](https://md.research.elamur.ai/free/en/papers/chen-2023-alpagasus-training-better-alpaca-fewer/) — Lichang Chen, Shiyang Li, Jun Yan, et al. · arXiv:2307.08701 - [AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference](https://md.research.elamur.ai/free/en/papers/yuan-2023-ambicoref-evaluating-human-model-sensitivity/) — Yuewei Yuan, Chaitanya Malaviya, Mark Yatskar · English - [AMR-TST: Abstract Meaning Representation-based Text Style Transfer](https://md.research.elamur.ai/free/en/papers/shi-2023-amr-tst-abstract-meaning-representation/) — Kaize Shi, Xueyao Sun, Li He, et al. · Machine translation - [An Open Dataset and Model for Language Identification](https://md.research.elamur.ai/free/en/papers/burchell-2023-open-dataset-model-language-identification/) — Laurie Burchell, Alexandra Birch, Nikolay Bogoychev, et al. · Proceedings of the 61st Annual Meeting of the… - [Analogy in Contact: Modeling Maltese Plural Inflection](https://md.research.elamur.ai/free/en/papers/lingbulk-2023-court-analogy-contact-modeling-maltese-plural/) — Sara Court, Andrea D. Sims, Micha Elsner · Semitic languages - [Analysis of Corpus-based Word-Order Typological Methods](https://md.research.elamur.ai/free/en/papers/alves-2023-analysis-corpus-based-word-order/) — Diego Alves, Božo Bekavac, Daniel Zeman, et al. · Proceedings of the Sixth Workshop on Universal… - [AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/) — Xingwei He, Zhenghao Lin, Yeyun Gong, et al. · arXiv:2303.16854 - [Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/gandhi-2023-annotating-mentions-alone-enables-efficient/) — Nupoor Gandhi, Anjalie Field, Emma Strubell · Proceedings of the 61st Annual Meeting of the… - [Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://md.research.elamur.ai/free/en/papers/cl-2023-klie-annotation-error-detection-analyzing-past/) — Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych · English - [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://md.research.elamur.ai/free/en/papers/liu-2023-annotation-inspired-implicit-discourse-relation/) — Wei Liu, Michael Strube · Proceedings of the 61st Annual Meeting of the… - [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://md.research.elamur.ai/free/en/papers/li-2023-api-bank-comprehensive-benchmark-tool/) — Minghao Li, Yingxiu Zhao, Bowen Yu, et al. · Proceedings of the 2023 Conference on Empirical… - [Are Emergent Abilities of Large Language Models a Mirage?](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/) — Rylan Schaeffer, Brando Miranda, Sanmi Koyejo · arXiv:2304.15004 - [Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/) — Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al. · Proceedings of the 19th Workshop on Multiword… - [Are idioms surprising?](https://md.research.elamur.ai/free/en/papers/philipp-2023-are-idioms-surprising/) — J. Nathanael Philipp, Michael Richter, Erik Daas, et al. · Proceedings of the 19th Conference on Natural… - [Are Large Language Models Robust Coreference Resolvers?](https://md.research.elamur.ai/free/en/papers/le-2023-large-language-models-robust-coreference/) — Nghia T. Le, Alan Ritter · arXiv:2305.14489 - [Are UD Treebanks Getting More Consistent? A Report Card for English UD](https://md.research.elamur.ai/free/en/papers/zeldes-2023-are-ud-treebanks-getting-more/) — Amir Zeldes, Nathan Schneider · Mova's core list - [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/) — Yong Cao, Li Zhou, Seolhwa Lee, et al. · Proceedings of the First Workshop on… - [Assessing the influence of attractor-verb distance on grammatical agreement in humans and language models](https://md.research.elamur.ai/free/en/papers/zacharopoulos-2023-assessing-influence-attractor-verb-distance/) — Christos Zacharopoulos, Théo Desbordes, Mathias Sablé-Meyer · Mova's core list - [AudioPaLM: A Large Language Model That Can Speak and Listen](https://md.research.elamur.ai/free/en/papers/rubenstein-2023-audiopalm-large-language-model-that/) — Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen, et al. · arXiv:2306.12925 - [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://md.research.elamur.ai/free/en/papers/dai-2023-auggpt-leveraging-chatgpt-text-data/) — Haixing Dai; Zhengliang Liu; Wenxiong Liao; Xiaoke Huang; Yihan Cao; Zihao Wu; Lin Zhao; Shaochen Xu; Wei Liu; Ninghao Liu; Sheng Li; Dajiang Zhu; Hongmin Cai; Lichao Sun; Quanzheng Li; Dinggang Shen; Tianming Liu;, Xiang Li · arXiv:2302.13007 - [Augmented Language Models: a Survey](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/) — Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al. · arXiv:2302.07842 - [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2023-autodan-generating-stealthy-jailbreak-prompts/) — Xiaogeng Liu, Nan Xu, Muhao Chen, et al. · arXiv:2310.04451 - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/) — Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al. · arXiv:2308.08155 - [Automated evaluation of written discourse coherence using GPT-4](https://md.research.elamur.ai/free/en/papers/naismith-2023-automated-evaluation-written-discourse-coherence/) — Ben Naismith, Phoebe Mulcaire, Jill Burstein · Proceedings of the 18th Workshop on Innovative… - [Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act](https://md.research.elamur.ai/free/en/papers/chen-2023-automatic-evaluate-dialogue-appropriateness-dialogue/) — Bao Chen, Yuanjie Wang, Zeming Liu, et al. · Findings of the Association for Computational… - [Automatic Evaluation and Analysis of Idioms in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/baziotis-2023-automatic-evaluation-analysis-idioms-neural/) — Christos Baziotis, Prashant Mathur, Eva Hasler · Proceedings of the 17th Conference of the… - [Automatic Prompt Optimization with “Gradient Descent” and Beam Search](https://md.research.elamur.ai/free/en/papers/pryzant-2023-automatic-prompt-optimization-gradient-descent/) — Reid Pryzant, Dan Iter, Jerry Li, et al. · Proceedings of the 2023 Conference on Empirical… - [Autonomous chemical research with large language models](https://md.research.elamur.ai/free/en/papers/boiko-2023-autonomous-chemical-research-large-language/) — Daniil A. Boiko, Robert MacKnight, Ben Kline, et al. · Nature - [Baichuan 2: Open Large-scale Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/) — Aiyuan Yang, Bin Xiao, Bingning Wang, et al. · arXiv:2309.10305 - [Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data](https://md.research.elamur.ai/free/en/papers/xu-2023-baize-open-source-chat-model/) — Canwen Xu, Daya Guo, Nan Duan, et al. · arXiv:2304.01196 - [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://md.research.elamur.ai/free/en/papers/jain-2023-baseline-defenses-adversarial-attacks-against/) — Neel Jain, Avi Schwarzschild, Yuxin Wen, et al. · arXiv:2309.00614 - [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/) — Jiaming Ji, Mickel Liu, Juntao Dai, et al. · arXiv:2307.04657 - [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/) — Jiawei Chen, Hongyu Lin, Xianpei Han, et al. · arXiv:2309.01431 - [Benchmarking Machine Translation with Cultural Awareness](https://md.research.elamur.ai/free/en/papers/yao-2023-benchmarking-machine-translation-cultural-awareness/) — Binwei Yao, Ming Jiang, Tara Bobinac, et al. · arXiv:2305.14328 - [Beyond Concatenative Morphology: Applying OntoLex-Morph to Maltese](https://md.research.elamur.ai/free/en/papers/aclbulk-2023-ionov-beyond-concatenative-morphology-applying-ontolex/) — Maxim Ionov, Mike Rosner · Semitic languages - [Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models](https://md.research.elamur.ai/free/en/papers/singh-2023-human-data-scaling-self-training/) — Avi Singh, John D. Co-Reyes, Rishabh Agarwal, et al. · arXiv:2312.06585 - [Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) — Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al. · arXiv:2309.00770 - [Biblical translation: do different strategies produce divergent texts?](https://md.research.elamur.ai/free/en/papers/bertos-2023-biblical-translation-do-different-strategies/) — Elena Serrano Bertos, Miguel Ángel Vega Cernuda · Machine translation - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-blip-2-bootstrapping-language-image/) — Junnan Li, Dongxu Li, Silvio Savarese, et al. · arXiv:2301.12597 - [BloombergGPT: A Large Language Model for Finance](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/) — Shijie Wu, Ozan Irsoy, Steven Lu, et al. · arXiv:2303.17564 - [Bridging Argument Quality and Deliberative Quality Annotations with Adapters](https://md.research.elamur.ai/free/en/papers/falk-2023-bridging-argument-quality-deliberative-quality/) — Neele Falk, Gabriella Lapesa · Findings of the Association for Computational… - [Bridging Background Knowledge Gaps in Translation with Automatic Explicitation](https://md.research.elamur.ai/free/en/papers/han-2023-bridging-background-knowledge-gaps-translation/) — HyoJung Han, Jordan Boyd-Graber, Marine Carpuat · Proceedings of the 2023 Conference on Empirical… - [Bridging Semantic Frameworks: mapping DRS onto AMR](https://md.research.elamur.ai/free/en/papers/pavlova-2023-bridging-semantic-frameworks-mapping-drs/) — Siyana Pavlova, Maxime Amblard, Bruno Guillaume · Machine translation - [Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation](https://md.research.elamur.ai/free/en/papers/guo-2023-bridging-gap-between-synthetic-authentic/) — Wenyu Guo, Qingkai Fang, Dong Yu, et al. · Proceedings of the 2023 Conference on Empirical… - [Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/) — Patrick Fernandes, Aman Madaan, Emmy Liu, et al. · Research library - [Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/) — Oskar Holmström, Jenny Kunz, Marco Kuhlmann · Proceedings of the Second Workshop on Resources… - [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://md.research.elamur.ai/free/en/papers/huang-2023-c-eval-multi-level-multi/) — Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, et al. · arXiv:2305.08322 - [C-Pack: Packed Resources For General Chinese Embeddings](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/) — Shitao Xiao, Zheng Liu, Peitian Zhang, et al. · arXiv:2309.07597 - [C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection](https://md.research.elamur.ai/free/en/papers/zhao-2023-c-stance-large-dataset-chinese/) — Chenye Zhao, Yingjie Li, Cornelia Caragea · Proceedings of the 61st Annual Meeting of the… - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/) — Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al. · arXiv:2303.17760 - [CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic](https://md.research.elamur.ai/free/en/papers/ud-2023-elshabrawy-camelparser2-0-state-art-dependency/) — Ahmed Elshabrawy, Muhammed AbuOdeh, Go Inoue, et al. · Semitic languages - [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/) — Hamish Ivison, Yizhong Wang, Valentina Pyatkin, et al. · arXiv:2311.10702 - [Can AI-Generated Text be Reliably Detected?](https://md.research.elamur.ai/free/en/papers/sadasivan-2023-ai-generated-text-reliably-detected/) — Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, et al. · arXiv:2303.11156 - [Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference !](https://md.research.elamur.ai/free/en/papers/tang-2023-can-diffusion-model-achieve-better/) — Zecheng Tang, Pinzheng Wang, Keyan Zhou, et al. · Findings of the Association for Computational… - [Can GPT-3 Perform Statutory Reasoning?](https://md.research.elamur.ai/free/en/papers/blair-2023-stanek-gpt-3-perform-statutory-reasoning/) — Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme · arXiv:2302.06100 - [Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) — Caleb Ziems, William Held, Omar Shaikh, et al. · arXiv:2305.03514 - [Can LLM Already Serve as A Database Interface? 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