--- title: "Papers of 2023 — page 3" 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/3/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2023/page/3/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2023 # Papers of 2023 — page 3 769 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [← prev](https://md.research.elamur.ai/free/en/papers/2023/page/2/) [1](https://md.research.elamur.ai/free/en/papers/2023/) [2](https://md.research.elamur.ai/free/en/papers/2023/page/2/) **3** [4](https://md.research.elamur.ai/free/en/papers/2023/page/4/) … [8](https://md.research.elamur.ai/free/en/papers/2023/page/8/) [next →](https://md.research.elamur.ai/free/en/papers/2023/page/4/) - [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. · English - [Distilling Calibrated Knowledge for Stance Detection](https://md.research.elamur.ai/free/en/papers/li-2023-distilling-calibrated-knowledge-stance-detection/) — Yingjie Li, Cornelia Caragea · Findings of the Association for Computational… - [Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) — Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan · Findings of the Association for Computational… - [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/) — Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al. · Findings of the Association for Computational… - [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 · Proceedings of the 61st Annual Meeting of the… - [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. · Proceedings of the 2023 Conference on Empirical… - [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. · Ancient Hebrew - [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. · Proceedings of the 2023 Conference on Empirical… - [DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/) — Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al. · arXiv:2309.03883 - [Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts](https://md.research.elamur.ai/free/en/papers/alghamdi-2023-domain-adaptation-arabic-machine-translation/) — Emad A. Alghamdi, Jezia Zakraoui, Fares A. Abanmy · arXiv:2309.12863 - [Dotless Representation of Arabic Text: Analysis and Modeling](https://md.research.elamur.ai/free/en/papers/al-2023-shaibani-dotless-representation-arabic-text-analysis/) — Maged S. Al-Shaibani, Irfan Ahmad · arXiv:2312.16104 - [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/) — Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al. · arXiv:2310.03714 - [Dual Cache for Long Document Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/guo-2023-dual-cache-long-document-neural/) — Qipeng Guo, Xiangkun Hu, Yue Zhang, et al. · Proceedings of the 61st Annual Meeting of the… - [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. · Proceedings of the 17th Conference of the… - [Dynamic Routing Transformer Network for Multimodal Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/tian-2023-dynamic-routing-transformer-network-multimodal/) — Yuan Tian, Nan Xu, Ruike Zhang, et al. · Proceedings of the 61st Annual Meeting of the… - [Effective Long-Context Scaling of Foundation Models](https://md.research.elamur.ai/free/en/papers/xiong-2023-effective-long-context-scaling-foundation/) — Wenhan Xiong, Jingyu Liu, Igor Molybog, et al. · arXiv:2309.16039 - [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://md.research.elamur.ai/free/en/papers/cui-2023-efficient-effective-text-encoding-chinese/) — Yiming Cui, Ziqing Yang, Xin Yao · arXiv:2304.08177 - [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. · English - [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/) — Nora Belrose, Igor Ostrovsky, Lev McKinney, et al. · arXiv:2303.08112 - [Em Direção à Anotação Sintatica - UD de Tweets do Mercado Financeiro](https://md.research.elamur.ai/free/en/papers/barbosa-2023-em-direcao-anotacao-sintatica-ud/) — Bryan K. S. Barbosa, Ariani Di-Felippo · Proceedings of the 2nd Edition of the Universal… - [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. · Proceedings of the 2023 Conference on Empirical… - [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. · English - [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://md.research.elamur.ai/free/en/papers/liang-2023-encouraging-divergent-thinking-large-language/) — Tian Liang, Zhiwei He, Wenxiang Jiao, et al. · arXiv:2305.19118 - [Endalew Assefa, Descriptive grammar of Ezha, a Gurage language of Ethiopia (Ethio-Semitic)](https://md.research.elamur.ai/free/en/papers/bulakh-2023-endalew-assefa-descriptive-grammar-ezha/) — Maria Bulakh · Linguistique et Langues Africaines 9 - [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://md.research.elamur.ai/free/en/papers/ding-2023-enhancing-chat-language-models-scaling/) — Ning Ding, Yulin Chen, Bokai Xu, et al. · arXiv:2305.14233 - [Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/) — Zhihong Shao, Yeyun Gong, Yelong Shen, et al. · arXiv:2305.15294 - [EPIC: Multi-Perspective Annotation of a Corpus of Irony](https://md.research.elamur.ai/free/en/papers/frenda-2023-epic-multi-perspective-annotation-corpus/) — Simona Frenda, Alessandro Pedrani, Valerio Basile, et al. · Proceedings of the 61st Annual Meeting of the… - [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/teodorescu-2023-evaluating-emotion-arcs-across-languages/) — Daniela Teodorescu, Saif Mohammad · Findings of the Association for Computational… - [Evaluating Large Language Models in Theory of Mind Tasks](https://md.research.elamur.ai/free/en/papers/kosinski-2023-evaluating-large-language-models-theory/) — Michal Kosinski · arXiv:2302.02083 - [Evaluating Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/) — Zishan Guo, Renren Jin, Chuang Liu, et al. · arXiv:2310.19736 - [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. · Proceedings of the 2023 Conference on Empirical… - [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. · Proceedings of the 61st Annual Meeting of the… - [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. · arXiv:2305.01020 - [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/) — Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al. · arXiv:2304.03439 - [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. · arXiv:2305.12474 - [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 · Findings of the Association for Computational… - [Exclaiming non-sententially: exclamative sluices and the (non-elliptical) nature of ellipsis constructions](https://md.research.elamur.ai/free/en/papers/ginzburg-2023-exclaiming-non-sententially-exclamative-sluices/) — Jonathan Ginzburg, Jong-Bok Kim · English - [Expectations over Unspoken Alternatives Predict Pragmatic Inferences](https://md.research.elamur.ai/free/en/papers/hu-2023-expectations-over-unspoken-alternatives-predict/) — Jennifer Hu, Roger Levy, Judith Degen, et al. · English - [Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) — Haiyan Zhao, Hanjie Chen, Fan Yang, et al. · arXiv:2309.01029 - [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://md.research.elamur.ai/free/en/papers/langstream-2023-gueta-explicit-morphological-knowledge-improves-pre/) — Eylon Gueta, Omer Goldman, Reut Tsarfaty · Modern Hebrew - [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 · English - [Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data](https://md.research.elamur.ai/free/en/papers/akhtar-2023-exploring-numerical-reasoning-capabilities-language/) — Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, et al. · Findings of the Association for Computational… - [Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness](https://md.research.elamur.ai/free/en/papers/rawte-2023-exploring-relationship-between-llm-hallucinations/) — Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, et al. · arXiv:2309.11064 - [Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian](https://md.research.elamur.ai/free/en/papers/saichyshyna-2023-extension-multi30k-multimodal-dataset-integrated/) — Nataliia Saichyshyna, Daniil Maksymenko, Oleksii Turuta, et al. · Proceedings of the Second Ukrainian Natural… - [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. · Proceedings of the 61st Annual Meeting of the… - [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://md.research.elamur.ai/free/en/papers/chern-2023-factool-factuality-detection-generative-ai/) — I-Chun Chern, Steffi Chern, Shiqi Chen, et al. · arXiv:2307.13528 - [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. · Proceedings of the 2023 Conference on Empirical… - [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. · arXiv:2310.05189 · Nat Mach Intell 6, 852--863… - [Faith and Fate: Limits of Transformers on Compositionality](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/) — Nouha Dziri, Ximing Lu, Melanie Sclar, et al. · arXiv:2305.18654 - [Faithful Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lyu-2023-faithful-chain-thought-reasoning/) — Qing Lyu, Shreya Havaldar, Adam Stein, et al. · Proceedings of the 13th International Joint… - [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. · arXiv:2310.05130 - [Feature Deletion by Head Movement: A New Solution to Agreement Asymmetries in Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/lingbulk-2023-himmelreich-feature-deletion-head-movement-new/) — Anke Himmelreich · Semitic languages - [Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations](https://md.research.elamur.ai/free/en/papers/markowska-2023-finding-common-ground-annotating-predicting/) — Magdalena Markowska, Mohammad Taghizadeh, Adil Soubki, et al. · Findings of the Association for Computational… - [FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN](https://md.research.elamur.ai/free/en/papers/agarwal-2023-findings-iwslt-2023-evaluation-campaign/) — Milind Agarwal, Sweta Agrawal, Antonios Anastasopoulos, et al. · Proceedings of the 20th International… - [Findings of the Second Shared Task on Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/abokrtsky-2023-findings-second-shared-task-multilingual/) — Zdeněk Žabokrtský, Miloslav Konopik, Anna Nedoluzhko, et al. · Proceedings of the CRAC 2023 Shared Task on… - [Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)](https://md.research.elamur.ai/free/en/papers/muller-2023-findings-second-wmt-shared-task/) — Mathias Müller, Malihe Alikhani, Eleftherios Avramidis, et al. · Proceedings of the Eighth Conference on Machine… - [Findings of the WMT 2023 Shared Task on Low-Resource Indic Language Translation](https://md.research.elamur.ai/free/en/papers/pal-2023-findings-wmt-2023-shared-task/) — Santanu Pal, Partha Pakray, Sahinur Rahman Laskar, et al. · Proceedings of the Eighth Conference on Machine… - [Findings of the WMT 2023 Shared Task on Quality Estimation](https://md.research.elamur.ai/free/en/papers/blain-2023-findings-wmt-2023-shared-task/) — Frederic Blain, Chrysoula Zerva, Ricardo Rei, et al. · Proceedings of the Eighth Conference on Machine… - [Fine-Grained Human Feedback Gives Better Rewards for Language Model Training](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/) — Zeqiu Wu, Yushi Hu, Weijia Shi, et al. · arXiv:2306.01693 - [Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization](https://md.research.elamur.ai/free/en/papers/al-2023-rfooh-fine-tashkeel-finetuning-byte-level/) — Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou · arXiv:2303.14588 - [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/) — Xiangyu Qi, Yi Zeng, Tinghao Xie, et al. · arXiv:2310.03693 - [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. · arXiv:2311.08401 - [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://md.research.elamur.ai/free/en/papers/ma-2023-fine-tuning-llama-multi-stage/) — Xueguang Ma, Liang Wang, Nan Yang, et al. · arXiv:2310.08319 - [FinGPT: Open-Source Financial Large Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-fingpt-open-source-financial-large/) — Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang · arXiv:2306.06031 - [FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/) — Ying Sheng, Lianmin Zheng, Binhang Yuan, et al. · arXiv:2303.06865 - [FOOCTTS: Generating Arabic Speech with Acoustic Environment for Football Commentator](https://md.research.elamur.ai/free/en/papers/baali-2023-fooctts-generating-arabic-speech-acoustic/) — Massa Baali, Ahmed Ali · arXiv:2306.07936 - [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://md.research.elamur.ai/free/en/papers/vu-2023-freshllms-refreshing-large-language-models/) — Tu Vu, Mohit Iyyer, Xuezhi Wang, et al. · arXiv:2310.03214 - [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. · Transactions of the Association for… - [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. · Proceedings of the 61st Annual Meeting of the… - [From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/) — Ming Li, Yong Zhang, Zhitao Li, et al. · arXiv:2308.12032 - [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. · arXiv:2310.03003 - [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://md.research.elamur.ai/free/en/papers/chen-2023-frugalgpt-use-large-language-models/) — Lingjiao Chen, Matei Zaharia, James Zou · arXiv:2305.05176 - [GAIA: a benchmark for General AI Assistants](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/) — Gregoire Mialon, Clementine Fourrier, Craig Swift, et al. · arXiv:2311.12983 - [GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4](https://md.research.elamur.ai/free/en/papers/kocmi-2023-gemba-mqm-detecting-translation-quality/) — Tom Kocmi, Christian Federmann · Proceedings of the Eighth Conference on Machine… - [Gemini: A Family of Highly Capable Multimodal Models](https://md.research.elamur.ai/free/en/papers/gemini-2023-team-gemini-family-highly-capable-multimodal/) — Gemini Team, Rohan Anil, Sebastian Borgeaud, et al. · arXiv:2312.11805 - [Generative Judge for Evaluating Alignment](https://md.research.elamur.ai/free/en/papers/li-2023-generative-judge-evaluating-alignment/) — Junlong Li, Shichao Sun, Weizhe Yuan, et al. · arXiv:2310.05470 - [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://md.research.elamur.ai/free/en/papers/treebanks-2023-aoyama-gentle-genre-diverse-multilayer-challenge/) — Tatsuya Aoyama, Shabnam Behzad, Luke Gessler, et al. · English - [Germanic syntax: A constraint-based view](https://md.research.elamur.ai/free/en/papers/muller-2023-germanic-syntax-constraint-based/) — Stefan Müller · English - [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/jiang-2023-global-local-hierarchy-aware-contrastive/) — Yuxin Jiang, Linhan Zhang, Wei Wang · Findings of the Association for Computational… - [GlotLID: Language Identification for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/) — Amir Hossein Kargaran, Ayyoob Imani, François Yvon, et al. · Findings of the Association for Computational… - [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://md.research.elamur.ai/free/en/papers/sainz-2023-gollie-annotation-guidelines-improve-zero/) — Oscar Sainz, Iker Garcia-Ferrero, Rodrigo Agerri, et al. · arXiv:2310.03668 - [Gorilla: Large Language Model Connected with Massive APIs](https://md.research.elamur.ai/free/en/papers/patil-2023-gorilla-large-language-model-connected/) — Shishir G. 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