--- title: "Research Triangle — works of 2023 — 19 papers in our library" description: "19 works of Research Triangle from 2023 in our library, the most cited first. Among them: PromptRobust: Towards Evaluating the Robustness…" canonical: "https://research.elamur.ai/free/en/agglomerations/research-triangle/year/2023/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/agglomerations/research-triangle/year/2023/) [home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [agglomerations](https://md.research.elamur.ai/free/en/agglomerations/) / [Research Triangle](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/) / 2023 # Works of Research Triangle from 2023 19 works · the most cited first · [← Research Triangle](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/) 1. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/) 2. **[An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 254 citations* 3. **[GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 184 citations* 4. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations* 5. [**Analyzing and Mitigating Object Hallucination in Large Vision-Language Models arXiv:2310.00754**Yiyang Zhou, Chenhang Cui, Jaehong Yoon, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-analyzing-mitigating-object-hallucination-large/) 6. [**Fine-tuning Language Models for Factuality arXiv:2311.08401**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/) 7. [**Formalizing and Benchmarking Prompt Injection Attacks and Defenses arXiv:2310.12815**Yupei Liu, Yuqi Jia, Runpeng Geng, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-formalizing-benchmarking-prompt-injection-attacks/) 8. **[Selectively Answering Ambiguous Questions](https://md.research.elamur.ai/free/en/papers/cole-2023-selectively-answering-ambiguous-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jeremy Cole, Michael Zhang, Daniel Gillick, et al.*2023 · 84 citations* 9. [**MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use arXiv:2310.03128**Yue Huang, Jiawen Shi, Yuan Li, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-metatool-benchmark-large-language-models/) 10. [**Punica: Multi-Tenant LoRA Serving MLSys 2024**Lequn Chen, Zihao Ye, Yongji Wu, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-punica-multi-tenant-lora-serving/) 11. **[ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://md.research.elamur.ai/free/en/papers/prasad-2023-receval-evaluating-reasoning-chains-correctness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Archiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.*2023 · 73 citations* 12. [**Branch-Solve-Merge Improves Large Language Model Evaluation and Generation arXiv:2310.15123**Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, et al.*2023 · 68 citations*](https://md.research.elamur.ai/free/en/papers/saha-2023-branch-solve-merge-improves-large/) 13. **[Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models](https://md.research.elamur.ai/free/en/papers/hase-2023-methods-measuring-updating-visualizing-factual/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Peter Hase, Mona Diab, Asli Celikyilmaz, et al.*2023 · 51 citations* 14. **[Seq2seq is All You Need for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/zhang-2023-seq2seq-is-all-you-need/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenzheng Zhang, Sam Wiseman, Karl Stratos*2023 · 32 citations* 15. [**Zero and Few-shot Semantic Parsing with Ambiguous Inputs arXiv:2306.00824**Elias Stengel-Eskin, Kyle Rawlins, Benjamin Van Durme*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/stengel-eskin-2023-zero-few-shot-semantic-parsing/) 16. **[Improving Automated Prediction of English Lexical Blends Through the Use of Observable Linguistic Features](https://md.research.elamur.ai/free/en/papers/saunders-2023-improving-automated-prediction-english-lexical/) [Workshop of the Special Interest Group on Computational Morphology and Phonology (SIGMORPHON)](https://md.research.elamur.ai/free/en/venues/sigmorphon/)**Jarem Saunders*2023 · 3 citations* 17. **[Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers](https://md.research.elamur.ai/free/en/papers/kumaran-2023-improving-classroom-dialogue-act-recognition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vikram Kumaran, Jonathan Rowe, Bradford Mott, et al.*2023 · 3 citations* 18. **[Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models](https://md.research.elamur.ai/free/en/papers/li-2023-pragmatic-reasoning-unlocks-quantifier-semantics/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yiyuan Li, Rakesh Menon, Sayan Ghosh, et al.*2023 · 2 citations* 19. [**The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation**Edwin Agnew, Michelle Qiu, Lily Zhu, et al.*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/agnew-2023-mechanical-bard-interpretable-machine-learning/) - [← back to](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/) — Research Triangle