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title: "Papers of 2023 — page 8"
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."
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# Papers of 2023 — page 8

769 papers · [all years](https://md.research.elamur.ai/free/en/papers/)

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- [Understanding and Bridging the Modality Gap for Speech Translation](https://md.research.elamur.ai/free/en/papers/fang-2023-understanding-bridging-modality-gap-speech/) — Qingkai Fang, Yang Feng · Proceedings of the 61st Annual Meeting of the…
- [Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/) — Liyan Tang, Tanya Goyal, Alex Fabbri, et al. · Proceedings of the 61st Annual Meeting of the…
- [Understanding the Effects of RLHF on LLM Generalisation and Diversity](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/) — Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al. · arXiv:2310.06452
- [Understanding Transformer Memorization Recall Through Idioms](https://md.research.elamur.ai/free/en/papers/haviv-2023-understanding-transformer-memorization-recall-through/) — Adi Haviv, Ido Cohen, Jacob Gidron, et al. · Proceedings of the 17th Conference of the…
- [UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language](https://md.research.elamur.ai/free/en/papers/xi-2023-unicorn-unified-cognitive-signal-reconstruction/) — Nuwa Xi, Sendong Zhao, Haochun Wang, et al. · Proceedings of the 61st Annual Meeting of the…
- [Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) — Xiaonan Li, Kai Lv, Hang Yan, et al. · Proceedings of the 61st Annual Meeting of the…
- [Unified Syntactic Annotation of English in the CGEL Framework](https://md.research.elamur.ai/free/en/papers/bank-2023-reynolds-unified-syntactic-annotation-english-cgel/) — Brett Reynolds, Aryaman Arora, Nathan Schneider · English
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) — Shirui Pan, Linhao Luo, Yufei Wang, et al. · arXiv:2306.08302 · IEEE Transactions on…
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/) — Andy Zou, Zifan Wang, Nicholas Carlini, et al. · arXiv:2307.15043
- [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/) — Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al. · arXiv:2307.05300
- [Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://md.research.elamur.ai/free/en/papers/honovich-2023-unnatural-instructions-tuning-language-models/) — Or Honovich, Thomas Scialom, Omer Levy, et al. · Proceedings of the 61st Annual Meeting of the…
- [Unpacking Ambiguous Structure: A Dataset for Ambiguous Implicit Discourse Relations for English and Egyptian Arabic](https://md.research.elamur.ai/free/en/papers/ruby-2023-unpacking-ambiguous-structure-dataset-ambiguous/) — Ahmed Ruby, Sara Stymne, Christian Hardmeier · Proceedings of the 4th Workshop on…
- [Unravelling Indirect Answers to Wh-Questions: Corpus Construction, Analysis, and Generation](https://md.research.elamur.ai/free/en/papers/yusupujiang-2023-unravelling-indirect-answers-wh-questions/) — Zulipiye Yusupujiang, Jonathan Ginzburg · English
- [Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study](https://md.research.elamur.ai/free/en/papers/baali-2023-unsupervised-data-selection-tts-using/) — Massa Baali, Tomoki Hayashi, Hamdy Mubarak, et al. · arXiv:2301.09099
- [Using LSTM and GRU With a New Dataset for Named Entity Recognition in the Arabic Language](https://md.research.elamur.ai/free/en/papers/shaker-2023-using-lstm-gru-new-dataset/) — Alaa Shaker, Alaa Aldarf, Igor Bessmertny · arXiv:2304.03399
- [Using Modern Languages to Parse Ancient Ones: a Test on Old English](https://md.research.elamur.ai/free/en/papers/villa-2023-modern-languages-parse-ancient-ones/) — Luca Brigada Villa, Martina Giarda · English
- [Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework](https://md.research.elamur.ai/free/en/papers/zhao-2023-verify-edit-knowledge-enhanced-chain/) — Ruochen Zhao, Xingxuan Li, Shafiq Joty, et al. · Proceedings of the 61st Annual Meeting of the…
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://md.research.elamur.ai/free/en/papers/maaz-2023-video-chatgpt-towards-detailed-video/) — Muhammad Maaz, Hanoona Rasheed, Salman Khan, et al. · arXiv:2306.05424
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://md.research.elamur.ai/free/en/papers/zhang-2023-video-llama-instruction-tuned-audio/) — Hang Zhang, Xin Li, Lidong Bing · arXiv:2306.02858
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://md.research.elamur.ai/free/en/papers/lin-2023-video-llava-learning-united-visual/) — Bin Lin, Yang Ye, Bin Zhu, et al. · arXiv:2311.10122
- [VideoChat: Chat-Centric Video Understanding](https://md.research.elamur.ai/free/en/papers/li-2023-videochat-chat-centric-video-understanding/) — KunChang Li, Yinan He, Yi Wang, et al. · arXiv:2305.06355
- [VILA: On Pre-training for Visual Language Models](https://md.research.elamur.ai/free/en/papers/lin-2023-vila-pre-training-visual-language/) — Ji Lin, Hongxu Yin, Wei Ping, et al. · arXiv:2312.07533
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://md.research.elamur.ai/free/en/papers/suris-2023-vipergpt-visual-inference-python-execution/) — Didac Suris, Sachit Menon, Carl Vondrick · arXiv:2303.08128
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://md.research.elamur.ai/free/en/papers/wu-2023-visual-chatgpt-talking-drawing-editing/) — Chenfei Wu, Shengming Yin, Weizhen Qi, et al. · arXiv:2303.04671
- [Visual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/) — Haotian Liu, Chunyuan Li, Qingyang Wu, et al. · arXiv:2304.08485
- [We Understand Elliptical Sentences, and Language Models should Too: A New Dataset for Studying Ellipsis and its Interaction with Thematic Fit](https://md.research.elamur.ai/free/en/papers/acl-2023-testa-we-understand-elliptical-sentences-language/) — Davide Testa, Emmanuele Chersoni, Alessandro Lenci · English
- [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/) — Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al. · arXiv:2312.09390
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://md.research.elamur.ai/free/en/papers/zhou-2023-webarena-realistic-web-environment-building/) — Shuyan Zhou, Frank F. Xu, Hao Zhu, et al. · arXiv:2307.13854
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ravi-2023-what-happens-before-after-multi/) — Sahithya Ravi, Chris Tanner, Raymond Ng, et al. · Proceedings of the 17th Conference of the…
- [What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-makes-good-data-alignment-comprehensive/) — Wei Liu, Weihao Zeng, Keqing He, et al. · arXiv:2312.15685
- [What to Make of make? Sense Distinctions for Light Verbs](https://md.research.elamur.ai/free/en/papers/kallini-2023-what-make-make-sense-distinctions/) — Julie Kallini, Christiane Fellbaum · Proceedings of the 12th Global Wordnet…
- [When Agreement Isn't Agreement: Deflected Agreement in Najdi Arabic as Aspect](https://md.research.elamur.ai/free/en/papers/lingbulk-2023-rouillier-when-agreement-isn-t-agreement/) — Connor Caston Rouillier · Semitic languages
- [When to generate hedges in peer-tutoring interactions](https://md.research.elamur.ai/free/en/papers/abulimiti-2023-when-generate-hedges-peer-tutoring/) — Alafate Abulimiti, Chloé Clavel, Justine Cassell · English
- [When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)](https://md.research.elamur.ai/free/en/papers/gubelmann-2023-when-truth-matters-addressing-pragmatic/) — Reto Gubelmann, Aikaterini-lida Kalouli, Christina Niklaus, et al. · Proceedings of the 12th Joint Conference on…
- [When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle](https://md.research.elamur.ai/free/en/papers/sieker-2023-when-your-language-model-cannot/) — Judith Sieker, Sina Zarrieß · English
- [Whose Opinions Do Language Models Reflect?](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/) — Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al. · arXiv:2303.17548
- [Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/) — Damai Dai, Yutao Sun, Li Dong, et al. · Findings of the Association for Computational…
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://md.research.elamur.ai/free/en/papers/liu-2023-why-can-t-discourse-parsing/) — Yang Janet Liu, Amir Zeldes · Proceedings of the 17th Conference of the…
- [Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://md.research.elamur.ai/free/en/papers/oh-2023-why-does-surprisal-larger-transformer/) — Byung-Doh Oh, William Schuler · Transactions of the Association for…
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://md.research.elamur.ai/free/en/papers/xu-2023-wizardlm-empowering-large-pre-trained/) — Can Xu, Qingfeng Sun, Kai Zheng, et al. · arXiv:2304.12244 · The Twelfth International…
- [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/) — Haipeng Luo, Qingfeng Sun, Can Xu, et al. · arXiv:2308.09583
- [xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://md.research.elamur.ai/free/en/papers/khan-2023-xcodeeval-large-scale-multilingual-multitask/) — Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al. · arXiv:2303.03004
- [XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/) — Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al. · arXiv:2308.01263
- [Yauti: A Tool for Morphosyntactic Analysis of Nheengatu within the Universal Dependencies Framework](https://md.research.elamur.ai/free/en/papers/alencar-2023-yauti-tool-morphosyntactic-analysis-nheengatu/) — Leonel Figueiredo de Alencar · Proceedings of the 14th Brazilian Symposium in…
- [Yiqtol as Evidential Strategy in Biblical Hebrew](https://md.research.elamur.ai/free/en/papers/hbo-2023-olivero-yiqtol-evidential-strategy-biblical-hebrew/) — Vladimir Olivero · Ancient Hebrew
- [Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation](https://md.research.elamur.ai/free/en/papers/wen-2023-zero-shot-few-shot-stance/) — Haoyang Wen, Alexander Hauptmann · Proceedings of the 61st Annual Meeting of the…
- [ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding](https://md.research.elamur.ai/free/en/papers/shaham-2023-zeroscrolls-zero-shot-benchmark-long/) — Uri Shaham, Maor Ivgi, Avia Efrat, et al. · Findings of the Association for Computational…
- [ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/straka-2023-fal-corpipe-crac-2023-larger/) — Milan Straka · Proceedings of the CRAC 2023 Shared Task on…
- [ニューラル記号推論における推論過程の教示方法](https://md.research.elamur.ai/free/en/papers/aoki-2023-how-teach-reasoning-process/) — 青木洋一, 工藤慧音, Ana Brassard, et al. · 言語処理学会 第29回年次大会 発表論文集 (NLP2023)
- [一种改进的汉语语义角色分类体系与标注实践](https://md.research.elamur.ai/free/en/papers/song-2023-fine-grained-annotated/) — 宋衡, 曹存根, 王亚, et al. · 中文信息学报…
- [与知识相结合的提示学习研究综述](https://md.research.elamur.ai/free/en/papers/bao-2023-prompt-learning-combined/) — 鲍琛龙, 吕明阳, 唐晋韬, et al. · 中文信息学报…
- [中文句子级性别无偏数据集构建及预训练语言模型的性别偏度评估](https://md.research.elamur.ai/free/en/papers/zhao-2023-construction-chinese-sentence/) — 赵继舜, 杜冰洁, 刘鹏远, et al. · 中文信息学报…
- [写作智能评测研究综述和发展前景](https://md.research.elamur.ai/free/en/papers/xue-2023-systematic-automated-essay/) — 薛嗣媛, 周建设, 任福继 · 中文信息学报…
- [名詞役割入れ替えに頑健な日本語含意関係認識](https://md.research.elamur.ai/free/en/papers/amemiya-2023-japanese-textual-entailment-robust/) — 雨宮正弥, 増川哲太, 仲田明良, et al. · 言語処理学会 第29回年次大会 発表論文集 (NLP2023)
- [在线教育环境中学习共同体研究综述](https://md.research.elamur.ai/free/en/papers/zhang-2023-learning-communities-online/) — 张俊涛, 杨先娣, 宋伟, et al. · 软件学报 (Journal of Software)
- [基于依存句法的中文语义模型及语义提取方法](https://md.research.elamur.ai/free/en/papers/wang-2023-chinese-semantic-model/) — 王佳琦, 韩军, 孙启童 · 中文信息学报…
- [基于依存结构学习的中学数学术语鲁棒抽取](https://md.research.elamur.ai/free/en/papers/luo-2023-dependency-structure-learning/) — 罗凯威, 罗文兵, 黄琪, et al. · 中文信息学报…
- [基于学习-推理的约束求解方法研究进展](https://md.research.elamur.ai/free/en/papers/zou-2023-methods-constraint-solving/) — 邹悦, 赖家洋, 张永刚 · 软件学报 (Journal of Software)
- [基于深度学习的代码生成方法研究进展](https://md.research.elamur.ai/free/en/papers/yang-2023-deep-learning-code/) — 杨泽洲, 陈思榕, 高翠芸, et al. · 软件学报 (Journal of Software)
- [基于深度学习的多文档机器阅读理解综述](https://md.research.elamur.ai/free/en/papers/gao-2023-multi-document-machine/) — 高峰, 倪建成, 高鹏, et al. · 中文信息学报…
- [基于神经网络模型的汉语文本难度分级](https://md.research.elamur.ai/free/en/papers/li-2023-chinese-readability-assessment/) — 李文彪, 吴云芳 · 中文信息学报…
- [基于语言模型增强的中文关系抽取方法](https://md.research.elamur.ai/free/en/papers/xue-2023-language-model-enhanced/) — 薛平, 李影, 吴中海 · 中文信息学报…
- [对话式论辩研究综述](https://md.research.elamur.ai/free/en/papers/wei-2023-dialogical-argumentation/) — 魏忠钰, 丁佳玙, 沈晨晨, et al. · 中文信息学报…
- [常识问答研究综述](https://md.research.elamur.ai/free/en/papers/fan-2023-commonsense-question-answering/) — 范怡帆, 邹博伟, 徐庆婷, et al. · 软件学报 (Journal of Software)
- [汉语增强依存句法自动转换研究](https://md.research.elamur.ai/free/en/papers/yu-2023-automatic-transformation-enhanced/) — 余婧思, 师佳璐, 杨麟儿, et al. · 中文信息学报…
- [汉语词语离合现象识别研究](https://md.research.elamur.ai/free/en/papers/zhou-2023-separated-word-recognition/) — 周露, 曲维光, 魏庭新, et al. · 中文信息学报…
- [第一届古代汉语分词和词性标注国际评测](https://md.research.elamur.ai/free/en/papers/li-2023-first-international-ancient/) — 李斌, 袁义国, 芦靖雅, et al. · 中文信息学报…
- [问答ChatGPT之后: 超大预训练模型的机遇和挑战](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/) — 卢经纬, 郭超, 戴星原, et al. · 自动化学报 (Acta Automatica Sinica)
- [面向链接预测的知识图谱表示学习方法综述](https://md.research.elamur.ai/free/en/papers/du-2023-representation-learning-methods/) — 杜雪盈, 刘名威, 沈立炜, et al. · 软件学报 (Journal of Software)

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