--- title: "Papers of 2023 — page 2" 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/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2023/page/2/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2023 # Papers of 2023 — page 2 635 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [1](https://md.research.elamur.ai/free/en/papers/2023/) **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/) - [Challenges and Applications of Large Language Models](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) — Jean Kaddour, Joshua Harris, Maximilian Mozes, et al. · arXiv:2307.10169 - [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) — Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al. · Findings of the Association for Computational… - [Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2023-chameleon-plug-play-compositional-reasoning/) — Pan Lu, Baolin Peng, Hao Cheng, et al. · arXiv:2304.09842 - [Character Coreference Resolution in Movie Screenplays](https://md.research.elamur.ai/free/en/papers/baruah-2023-character-coreference-resolution-movie-screenplays/) — Sabyasachee Baruah, Shrikanth Narayanan · Findings of the Association for Computational… - [Character-LLM: A Trainable Agent for Role-Playing](https://md.research.elamur.ai/free/en/papers/shao-2023-character-llm-trainable-agent-role/) — Yunfan Shao, Linyang Li, Junqi Dai, et al. · Proceedings of the 2023 Conference on Empirical… - [ChatDev: Communicative Agents for Software Development](https://md.research.elamur.ai/free/en/papers/qian-2023-chatdev-communicative-agents-software/) — Chen Qian, Wei Liu, Hongzhang Liu, et al. · arXiv:2307.07924 - [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://md.research.elamur.ai/free/en/papers/li-2023-chatdoctor-medical-chat-model-fine/) — Yunxiang Li, Zihan Li, Kai Zhang, et al. · arXiv:2303.14070 - [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://md.research.elamur.ai/free/en/papers/chan-2023-chateval-better-llm-based/) — Chi-Min Chan, Weize Chen, Yusheng Su, et al. · arXiv:2308.07201 - [ChatGPT as a Factual Inconsistency Evaluator for Text Summarization](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/) — Zheheng Luo, Qianqian Xie, Sophia Ananiadou · arXiv:2303.15621 - [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://md.research.elamur.ai/free/en/papers/lai-2023-chatgpt-beyond-english-comprehensive-evaluation/) — Viet Dac Lai, Nghia Ngo, Amir Pouran Ben Veyseh, et al. · Findings of the Association for Computational… - [ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/) — Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al. · Research library - [ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages](https://md.research.elamur.ai/free/en/papers/robinson-2023-chatgpt-mt-competitive-high-but/) — Nathaniel Robinson, Perez Ogayo, David R. Mortensen, et al. · Proceedings of the Eighth Conference on Machine… - [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://md.research.elamur.ai/free/en/papers/gilardi-2023-chatgpt-outperforms-crowd-workers-text/) — Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli · arXiv:2303.15056 - [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://md.research.elamur.ai/free/en/papers/liu-2023-chatgpt-vs-human-authored-text/) — Dongqi Liu, Vera Demberg · arXiv:2306.07799 - [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://md.research.elamur.ai/free/en/papers/wei-2023-chatie-zero-shot-information-extraction/) — Xiang Wei; Xingyu Cui; Ning Cheng; Xiaobin Wang; Xin Zhang; Shen Huang; Pengjun Xie; Jinan Xu; Yufeng Chen; Meishan Zhang; Yong Jiang;, Wenjuan Han · arXiv:2302.10205 - [Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture](https://md.research.elamur.ai/free/en/papers/cui-2023-chatlaw-multi-agent-legal-assistant/) — Jiaxi Cui; Munan Ning; Zongjian Li; Bohua Chen; Yang Yan; Hao Li; Bin Ling; Yonghong Tian;, Li Yuan · arXiv:2306.16092 - [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/) — Baolin Peng, Michel Galley, Pengcheng He, et al. · arXiv:2302.12813 - [Chinese Idiom Paraphrasing](https://md.research.elamur.ai/free/en/papers/qiang-2023-chinese-idiom-paraphrasing/) — Jipeng Qiang, Yang Li, Chaowei Zhang, et al. · Transactions of the Association for… - [CMMLU: Measuring massive multitask language understanding in Chinese](https://md.research.elamur.ai/free/en/papers/li-2023-cmmlu-measuring-massive-multitask-language/) — Haonan Li, Yixuan Zhang, Fajri Koto, et al. · arXiv:2306.09212 - [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://md.research.elamur.ai/free/en/papers/zheng-2023-codegeex-pre-trained-model-code/) — Qinkai Zheng, Xiao Xia, Xu Zou, et al. · arXiv:2303.17568 - [CogAgent: A Visual Language Model for GUI Agents](https://md.research.elamur.ai/free/en/papers/hong-2023-cogagent-visual-language-model/) — Wenyi Hong, Weihan Wang, Qingsong Lv, et al. · arXiv:2312.08914 - [CogVLM: Visual Expert for Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/) — Weihan Wang, Qingsong Lv, Wenmeng Yu, et al. · arXiv:2311.03079 - [Compressing Context to Enhance Inference Efficiency of Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-compressing-context-enhance-inference-efficiency/) — Yucheng Li, Bo Dong, Frank Guerin, et al. · Proceedings of the 2023 Conference on Empirical… - [Concessive constructions in varieties of English](https://md.research.elamur.ai/free/en/papers/schutzler-2023-concessive-constructions-varieties-english/) — Ole Schützler · English - [Conjunct Lengths in English, Dependency Length Minimization, and Dependency Structure of Coordination](https://md.research.elamur.ai/free/en/papers/przepiorkowski-2023-conjunct-lengths-english-dependency-length/) — Adam Przepiórkowski, Michał Woźniak · Mova's core list - [Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wu-2023-connective-prediction-implicit-discourse-relation/) — Hongyi Wu, Hao Zhou, Man Lan, et al. · Proceedings of the 61st Annual Meeting of the… - [Coreference Resolution through a seq2seq Transition-Based System](https://md.research.elamur.ai/free/en/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/) — Bernd Bohnet, Chris Alberti, Michael Collins · Research library - [Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://md.research.elamur.ai/free/en/papers/lex-2023-weissweiler-chatgpt-wugs-morphology/) — Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban · English - [CQE: A Comprehensive Quantity Extractor](https://md.research.elamur.ai/free/en/papers/almasian-2023-cqe-comprehensive-quantity-extractor/) — Satya Almasian, Vivian Kazakova, Philipp Göldner, et al. · Proceedings of the 2023 Conference on Empirical… - [CREPE: Open-Domain Question Answering with False Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/) — Xinyan Yu, Sewon Min, Luke Zettlemoyer, et al. · English - [CRFに基づく形態素解析器のスコア計算の分割によるモデルサイズと解析速度の調整](https://md.research.elamur.ai/free/en/papers/akabe-2023-splitting-score-computation-crf/) — 赤部晃一, 神田峻介, 小田悠介 · 言語処理学会 第29回年次大会 発表論文集 (NLP2023) - [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/) — Zhibin Gou, Zhihong Shao, Yeyun Gong, et al. · arXiv:2305.11738 - [Cross-Document Event Coreference Resolution on Discourse Structure](https://md.research.elamur.ai/free/en/papers/chen-2023-cross-document-event-coreference-resolution/) — Xinyu Chen, Sheng Xu, Peifeng Li, et al. · Proceedings of the 2023 Conference on Empirical… - [Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting](https://md.research.elamur.ai/free/en/papers/liu-2023-crossing-threshold-idiomatic-machine-translation/) — Emmy Liu, Aditi Chaudhary, Graham Neubig · Proceedings of the 2023 Conference on Empirical… - [Culturally Aware Natural Language Inference](https://md.research.elamur.ai/free/en/papers/huang-2023-culturally-aware-natural-language-inference/) — Jing Huang, Diyi Yang · Findings of the Association for Computational… - [CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/) — Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al. · arXiv:2309.09400 - [DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/) — Yanchen Liu, William Held, Diyi Yang · English - [Data-driven Parsing Evaluation for Child-Parent Interactions](https://md.research.elamur.ai/free/en/papers/ud-2023-liu-data-driven-parsing-evaluation-child/) — Zoey Liu, Emily Prud'hommeaux · English - [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) — Boxin Wang, Weixin Chen, Hengzhi Pei, et al. · arXiv:2306.11698 - [Deductive Verification of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/) — Zhan Ling, Yunhao Fang, Xuanlin Li, et al. · arXiv:2306.03872 - [Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy](https://md.research.elamur.ai/free/en/papers/hbo-2023-aioanei-deep-aramaic-towards-synthetic-data/) — Andrei C. Aioanei, Regine Hunziker-Rodewald, Konstantin Klein, et al. · Ancient Hebrew - [DeepInception: Hypnotize Large Language Model to Be Jailbreaker](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/) — Xuan Li, Zhanke Zhou, Jianing Zhu, et al. · arXiv:2311.03191 - [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://md.research.elamur.ai/free/en/papers/jacobs-2023-deepspeed-ulysses-system-optimizations-enabling/) — Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, et al. · arXiv:2309.14509 - [Dependency Dialogue Acts — Annotation Scheme and Case Study](https://md.research.elamur.ai/free/en/papers/cai-2023-dependency-dialogue-acts-annotation-scheme/) — Jon Z. Cai, Brendan King, Margaret Perkoff, et al. · English - [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://md.research.elamur.ai/free/en/papers/wang-2023-describe-explain-plan-select-interactive/) — Zihao Wang, Shaofei Cai, Guanzhou Chen, et al. · arXiv:2302.01560 - [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://md.research.elamur.ai/free/en/papers/pyatkin-2023-design-choices-crowdsourcing-implicit-discourse/) — Valentina Pyatkin, Frances Yung, Merel C. J. Scholman, et al. · Research library - [Designing the LECOR Learner Corpus for Romanian](https://md.research.elamur.ai/free/en/papers/barbu-2023-designing-lecor-learner-corpus-romanian/) — Ana Maria Barbu, Elena Irimia, Carmen Mîrzea Vasile, et al. · Proceedings of the 14th International… - [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/) — Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al. · arXiv:2301.11305 - [Detecting Language Model Attacks with Perplexity](https://md.research.elamur.ai/free/en/papers/alon-2023-detecting-language-model-attacks-perplexity/) — Gabriel Alon, Michael Kamfonas · arXiv:2308.14132 - [DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://md.research.elamur.ai/free/en/papers/arxivrecent-2023-shmidman-dictabert-state-art-bert-suite/) — Shaltiel Shmidman, Avi Shmidman, Moshe Koppel · Modern Hebrew - [Difficulties in Handling Mathematical Expressions in Universal Dependencies](https://md.research.elamur.ai/free/en/papers/ud-2023-levine-difficulties-handling-mathematical-expressions-universal/) — Lauren Levine · English - [DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models](https://md.research.elamur.ai/free/en/papers/gong-2023-diffuseq-v2-bridging-discrete-continuous/) — Shansan Gong, Mukai Li, Jiangtao Feng, et al. · Findings of the Association for Computational… - [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://md.research.elamur.ai/free/en/papers/pourreza-2023-din-sql-decomposed-context-learning/) — Mohammadreza Pourreza, Davood Rafiei · arXiv:2304.11015 - [DINOv2: Learning Robust Visual Features without Supervision](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/) — Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al. · arXiv:2304.07193 - [Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting](https://md.research.elamur.ai/free/en/papers/chen-2023-dipping-plms-sauce-bridging-structure/) — Chen Chen, Yufei Wang, Aixin Sun, et al. · Findings of the Association for Computational… - [DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification](https://md.research.elamur.ai/free/en/papers/anuranjana-2023-discoflan-instruction-fine-tuning-refined/) — Kaveri Anuranjana · Proceedings of the 3rd Shared Task on Discourse… - [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/) — Chunkit Chan, Xin Liu, Jiayang Cheng, et al. · Findings of the Association for Computational… - [DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence](https://md.research.elamur.ai/free/en/papers/zhao-2023-discoscore-evaluating-text-generation-bert/) — Wei Zhao, Michael Strube, Steffen Eger · Proceedings of the 17th Conference of the… - [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… - [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 - [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… - [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 - [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 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… - [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… - [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… [1](https://md.research.elamur.ai/free/en/papers/2023/) **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/)