---
title: "Who cites Melanie Subbiah — page 7 — 11,345 citing papers"
description: "11,345 papers of the Elamur Research library cite the works of Melanie Subbiah, found in their bibliographies · page 7 of 20 · 601–700; the…"
canonical: "https://research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/7/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/7/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Melanie Subbiah](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/) / [cited by](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/) / page 7

# Who cites Melanie Subbiah

11,345 citations of their works from 11,345 papers of our library (of the 149,439 whose bibliographies we read) · 1,982 with a page here, the most cited first · page 7 of 20 · 601–700 · [← Melanie Subbiah](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/)

[← prev](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/6/) [1](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/) … [6](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/6/) **7** [8](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/8/) [9](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/9/) [10](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/10/) … [20](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/20/) [next →](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/8/)

601. [**AgentTuning: Enabling Generalized Agent Abilities for LLMs arXiv:2310.12823**Aohan Zeng, Mingdao Liu, Rui Lu, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2023-agenttuning-enabling-generalized-agent/)
602. **[FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts](https://md.research.elamur.ai/free/en/papers/gong-2023-figstep-jailbreaking-large-vision-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yichen Gong, Delong Ran, Jinyuan Liu, et al.*2023 · 163 citations*
603. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
604. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization arXiv:2303.15621**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
605. [**From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning arXiv:2308.12032**Ming Li, Yong Zhang, Zhitao Li, et al.*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/)
606. [**Reasoning with Language Model Prompting: A Survey arXiv:2212.09597**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
607. **[Larger-Scale Transformers for Multilingual Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 161 citations*
608. [**PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation arXiv:2104.12369**Wei Zeng, Xiaozhe Ren, Teng Su, et al.*2021 · 161 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2021-pangu-alpha-large-scale/)
609. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
610. **[In-Context Learning Creates Task Vectors](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Roee Hendel, Mor Geva, Amir Globerson*2023 · 159 citations*
611. **[Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering](https://md.research.elamur.ai/free/en/papers/wu-2022-self-adaptive-context-learning-information/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, et al.*2022 · 159 citations*
612. **[MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models](https://md.research.elamur.ai/free/en/papers/wen-2024-mindmap-knowledge-graph-prompting-sparks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilin Wen, Zifeng Wang, Jimeng Sun*2024 · 158 citations*
613. **[Large Language Models with Controllable Working Memory](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-controllable-working/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daliang Li, Ankit Singh Rawat, Manzil Zaheer, et al.*2023 · 158 citations*
614. **[The Devil Is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-devil-is-errors-leveraging-large/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, et al.*2023 · 158 citations*
615. **[TheoremQA: A Theorem-driven Question Answering Dataset](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenhu Chen, Ming Yin, Max Ku, et al.*2023 · 158 citations*
616. [**MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs arXiv:2402.15627**Ziheng Jiang, Haibin Lin, Yinmin Zhong, et al.*2024 · 157 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-megascale-scaling-large-language-model/)
617. [**Moshi: a speech-text foundation model for real-time dialogue arXiv:2410.00037**Alexandre Defossez, Laurent Mazare, Manu Orsini, et al.*2024 · 157 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2024-moshi-speech-text-foundation-model/)
618. **[Are aligned neural networks adversarially aligned?](https://md.research.elamur.ai/free/en/papers/carlini-2023-aligned-neural-networks-adversarially-aligned/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, et al.*2023 · 157 citations*
619. **[Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/louis-2023-interpretable-long-form-legal-question/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Antoine Louis, Gijs van Dijck, Gerasimos Spanakis*2023 · 157 citations*
620. **[Locally Typical Sampling](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Clara Meister, Tiago Pimentel, Gian Wiher, et al.*2023 · 157 citations*
621. [**Leveraging Large Language Models for Multiple Choice Question Answering arXiv:2210.12353**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 157 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
622. **[Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist](https://md.research.elamur.ai/free/en/papers/karpinska-2023-large-language-models-effectively-leverage/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Marzena Karpinska, Mohit Iyyer*2023 · 156 citations*
623. [**REFINER: Reasoning Feedback on Intermediate Representations arXiv:2304.01904**Debjit Paul, Mete Ismayilzada, Maxime Peyrard, et al.*2023 · 156 citations*](https://md.research.elamur.ai/free/en/papers/paul-2023-refiner-reasoning-feedback-intermediate-representations/)
624. **[Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://md.research.elamur.ai/free/en/papers/abadji-2022-towards-cleaner-document-oriented-multilingual/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, et al.*2022 · 156 citations*
625. **[Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/zhang-2024-extract-define-canonicalize-llm-based/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bowen Zhang, Harold Soh*2024 · 155 citations*
626. [**ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning arXiv:2212.07919**Olga Golovneva, Moya Chen, Spencer Poff, et al.*2022 · 155 citations*](https://md.research.elamur.ai/free/en/papers/golovneva-2022-roscoe-suite-metrics-scoring-step/)
627. [**ART: Automatic multi-step reasoning and tool-use for large language models arXiv:2303.09014**Bhargavi Paranjape, Scott Lundberg, Sameer Singh, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/paranjape-2023-art-automatic-multi-step-reasoning/)
628. [**Can GPT-3 Perform Statutory Reasoning? arXiv:2302.06100**Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/blair-stanek-2023-gpt-3-perform-statutory-reasoning/)
629. **[The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-dawn-after-dark-empirical-study/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyi Li, Jie Chen, Ruiyang Ren, et al.*2024 · 153 citations*
630. **[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/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Nathaniel Robinson, Perez Ogayo, David R. Mortensen, et al.*2023 · 153 citations*
631. **[Fine-Tuning Language Models with Just Forward Passes](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sadhika Malladi, Tianyu Gao, Eshaan Nichani, et al.*2023 · 153 citations*
632. **[Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning](https://md.research.elamur.ai/free/en/papers/wang-2023-label-words-anchors-information-flow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lean Wang, Lei Li, Damai Dai, et al.*2023 · 153 citations*
633. **[Memory-Efficient Pipeline-Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/narayanan-2020-memory-efficient-pipeline-parallel-dnn/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, et al.*2020 · 153 citations*
634. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators arXiv:2303.16854**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
635. **[V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/wu-2023-v-guided-visual-search-core/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Penghao Wu, Saining Xie*2023 · 152 citations*
636. **[Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://md.research.elamur.ai/free/en/papers/dou-2022-is-gpt-3-text-indistinguishable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, et al.*2022 · 152 citations*
637. **[ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hyunwoo Kim, Youngjae Yu, Liwei Jiang, et al.*2022 · 152 citations*
638. **[How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 151 citations*
639. **[Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction](https://md.research.elamur.ai/free/en/papers/tan-2022-revisiting-docred-addressing-false-negative/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qingyu Tan, Lu Xu, Lidong Bing, et al.*2022 · 151 citations*
640. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/)
641. [**Generation-Augmented Retrieval for Open-domain Question Answering arXiv:2009.08553**Yuning Mao, Pengcheng He, Xiaodong Liu, et al.*2020 · 151 citations*](https://md.research.elamur.ai/free/en/papers/mao-2020-generation-augmented-retrieval-open-domain/)
642. **[M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-m3exam-multilingual-multimodal-multilevel-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao, et al.*2023 · 150 citations*
643. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset arXiv:2303.03915**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
644. [**A Review on Language Models as Knowledge Bases arXiv:2204.06031**Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-review-language-models-knowledge-bases/)
645. **[CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://md.research.elamur.ai/free/en/papers/zhang-2020-cpm-large-scale-generative/) [AI Open](https://md.research.elamur.ai/free/en/venues/ai-open/)**Zhengyan Zhang, Xu Han, Hao Zhou, et al.*2020 · 150 citations*
646. **[OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models](https://md.research.elamur.ai/free/en/papers/lee-2023-owq-outlier-aware-weight-quantization/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Changhun Lee, Jungyu Jin, Taesu Kim, et al.*2023 · 149 citations*
647. [**SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models arXiv:2307.10635**Xiaoxuan Wang, Ziniu Hu, Pan Lu, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-scibench-evaluating-college-level-scientific/)
648. **[Self-Evaluation Guided Beam Search for Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 149 citations*
649. [**MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge arXiv:2206.08853**Linxi Fan, Guanzhi Wang, Yunfan Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/)
650. [**TALM: Tool Augmented Language Models arXiv:2205.12255**Aaron Parisi, Yao Zhao, Noah Fiedel*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2022-talm-tool-augmented-language-models/)
651. **[Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 148 citations*
652. **[From Slow Bidirectional to Fast Autoregressive Video Diffusion Models](https://md.research.elamur.ai/free/en/papers/yin-2024-slow-bidirectional-fast-autoregressive-video/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Tianwei Yin, Qiang Zhang, Richard Zhang, et al.*2024 · 148 citations*
653. **[Context-faithful Prompting for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 148 citations*
654. [**Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature arXiv:2310.05130**Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.*2023 · 148 citations*](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/)
655. **[Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.*2023 · 148 citations*
656. **[GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction](https://md.research.elamur.ai/free/en/papers/yang-2023-gpt4tools-teaching-large-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rui Yang, Lin Song, Yanwei Li, et al.*2023 · 147 citations*
657. **[ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://md.research.elamur.ai/free/en/papers/hao-2023-toolkengpt-augmenting-frozen-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shibo Hao, Tianyang Liu, Zhen Wang, et al.*2023 · 147 citations*
658. [**Is GPT-3 a Good Data Annotator? arXiv:2212.10450**Bosheng Ding, Chengwei Qin, Linlin Liu, et al.*2022 · 147 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/)
659. **[Fact-Checking Complex Claims with Program-Guided Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liangming Pan, Xiaobao Wu, Xinyuan Lu, et al.*2023 · 146 citations*
660. **[Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaonan Li, Kai Lv, Hang Yan, et al.*2023 · 146 citations*
661. **[How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*
662. **[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*
663. [**Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation arXiv:2304.01746**Tao Fang, Shu Yang, Kaixin Lan, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/fang-2023-chatgpt-highly-fluent-grammatical-error/)
664. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges arXiv:2310.17631**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
665. **[SpotServe: Serving Generative Large Language Models on Preemptible Instances](https://md.research.elamur.ai/free/en/papers/miao-2023-spotserve-serving-generative-large-language/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Xupeng Miao, Chunan Shi, Jiangfei Duan, et al.*2023 · 145 citations*
666. **[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/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruochen Zhao, Xingxuan Li, Shafiq Joty, et al.*2023 · 145 citations*
667. **[Dynamic Prefix-Tuning for Generative Template-based Event Extraction](https://md.research.elamur.ai/free/en/papers/liu-2022-dynamic-prefix-tuning-generative-template/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiao Liu, Heyan Huang, Ge Shi, et al.*2022 · 145 citations*
668. **[TopiOCQA: Open-domain Conversational Question Answering with Topic Switching](https://md.research.elamur.ai/free/en/papers/adlakha-2022-topiocqa-open-domain-conversational-question/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Vaibhav Adlakha, Shehzaad Dhuliawala, Kaheer Suleman, et al.*2022 · 145 citations*
669. **[Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Chung, Ece Kamar, Saleema Amershi*2023 · 144 citations*
670. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
671. [**SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills arXiv 2023**Amey Agrawal, Ashish Panwar, Jayashree Mohan, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2023-sarathi-efficient-llm-inference-piggybacking/)
672. [**Efficient Training of Language Models to Fill in the Middle arXiv:2207.14255**Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
673. **[In-Context Learning for Few-Shot Dialogue State Tracking](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, et al.*2022 · 144 citations*
674. **[Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://md.research.elamur.ai/free/en/papers/wang-2022-iteratively-prompt-pre-trained-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Boshi Wang, Xiang Deng, Huan Sun*2022 · 144 citations*
675. [**A Roadmap to Pluralistic Alignment arXiv:2402.05070**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
676. [**A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation arXiv:2307.03987**Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/)
677. [**Rethinking with Retrieval: Faithful Large Language Model Inference arXiv:2301.00303**Hangfeng He, Hongming Zhang, Dan Roth*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-rethinking-retrieval-faithful-large-language/)
678. [**Understanding the Effects of RLHF on LLM Generalisation and Diversity arXiv:2310.06452**Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/)
679. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations*
680. [**Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness arXiv:2304.11633**Bo Li, Gexiang Fang, Yang Yang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/)
681. [**ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics arXiv:2302.12433**Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-proofnet-autoformalizing-formally-proving-undergraduate/)
682. **[A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 142 citations*
683. [**Rethinking embedding coupling in pre-trained language models arXiv:2010.12821**Hyung Won Chung, Thibault Févry, Henry Tsai, et al.*2020 · 142 citations*](https://md.research.elamur.ai/free/en/papers/chung-2020-rethinking-embedding-coupling-pre-trained/)
684. **[Diverse Demonstrations Improve In-context Compositional Generalization](https://md.research.elamur.ai/free/en/papers/levy-2023-diverse-demonstrations-improve-context-compositional/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Itay Levy, Ben Bogin, Jonathan Berant*2023 · 141 citations*
685. [**Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization arXiv:2302.08081**Xianjun Yang, Yan Li, Xinlu Zhang, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-exploring-limits-chatgpt-query-aspect/)
686. **[Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://md.research.elamur.ai/free/en/papers/wen-2023-hard-prompts-made-easy-gradient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Wen, Neel Jain, John Kirchenbauer, et al.*2023 · 141 citations*
687. **[Retrieval-based Language Models and Applications](https://md.research.elamur.ai/free/en/papers/asai-2023-retrieval-based-language-models-applications/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Akari Asai, Sewon Min, Zexuan Zhong, et al.*2023 · 141 citations*
688. [**SLiC-HF: Sequence Likelihood Calibration with Human Feedback arXiv:2305.10425**Yao Zhao, Rishabh Joshi, Tianqi Liu, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-slic-hf-sequence-likelihood-calibration/)
689. **[Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases](https://md.research.elamur.ai/free/en/papers/cao-2021-knowledgeable-or-educated-guess-revisiting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2021 · 141 citations*
690. **[Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design](https://md.research.elamur.ai/free/en/papers/tjuatja-2024-do-llms-exhibit-human-like/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Lindia Tjuatja, Valerie Chen, Tongshuang Wu, et al.*2024 · 140 citations*
691. **[MLVU: Benchmarking Multi-task Long Video Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2024-mlvu-benchmarking-multi-task-long/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Junjie Zhou, Yan Shu, Bo Zhao, et al.*2024 · 140 citations*
692. [**Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration arXiv:2307.05300**Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.*2023 · 140 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/)
693. **[NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ximing Lu, Sean Welleck, Peter West, et al.*2022 · 140 citations*
694. **[TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://md.research.elamur.ai/free/en/papers/uchendu-2021-turingbench-benchmark-environment-turing-test/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adaku Uchendu, Zeyu Ma, Thai Le, et al.*2021 · 140 citations*
695. [**Agent Laboratory: Using LLM Agents as Research Assistants arXiv:2501.04227**Samuel Schmidgall, Yusheng Su, Ze Wang, et al.*2025 · 139 citations*](https://md.research.elamur.ai/free/en/papers/schmidgall-2025-agent-laboratory-using-llm/)
696. [**Exploring Length Generalization in Large Language Models arXiv:2207.04901**Cem Anil, Yuhuai Wu, Anders Andreassen, et al.*2022 · 139 citations*](https://md.research.elamur.ai/free/en/papers/anil-2022-exploring-length-generalization-large-language/)
697. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models arXiv:2212.08037**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 138 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
698. **[DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models](https://md.research.elamur.ai/free/en/papers/su-2024-dragin-dynamic-retrieval-augmented-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weihang Su, Yichen Tang, Qingyao Ai, et al.*2024 · 137 citations*
699. **[Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*
700. [**RAFT: Adapting Language Model to Domain Specific RAG arXiv:2403.10131**Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/)

[← prev](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/6/) [1](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/) … [6](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/6/) **7** [8](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/8/) [9](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/9/) [10](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/10/) … [20](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/20/) [next →](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/cited-by/page/8/)

- [← back to](https://md.research.elamur.ai/free/en/authors/melanie-subbiah/) — Melanie Subbiah