🧑‍🔬 Who cites Parag Singla

105 citations of their works from 105 papers of our library (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · ← Parag Singla

1. OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationarXiv:2212.12017Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.2022 · 244 citations
2. Knowledge Conflicts for LLMs: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.2024 · 182 citations
3. ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Justin Chen, Swarnadeep Saha, Mohit Bansal2024 · 156 citations
4. ROSCOE: A Suite of Metrics for Scoring Step-by-Step ReasoningarXiv:2212.07919Olga Golovneva, Moya Chen, Spencer Poff, et al.2022 · 155 citations
5. RA-DIT: Retrieval-Augmented Dual Instruction TuningarXiv:2310.01352Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.2023 · 149 citations
6. SCOTT: Self-Consistent Chain-of-Thought DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Peifeng Wang, Zhengyang Wang, Zheng Li, et al.2023 · 146 citations
7. Few-Shot Self-Rationalization with Natural Language PromptsFindings of the Association for Computational LinguisticsAna Marasovic, Iz Beltagy, Doug Downey, et al.2022 · 134 citations
8. The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-TuningConference on Empirical Methods in Natural Language Processing (EMNLP)Seungone Kim, Se Joo, Doyoung Kim, et al.2023 · 118 citations
9. ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language ModelsLanguage Resources and Evaluation Conference (LREC)Ning Bian, Xianpei Han, Le Sun, et al.2024 · 106 citations
10. Complementary Explanations for Effective In-Context LearningFindings of the Association for Computational LinguisticsXi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.2023 · 106 citations
11. ReCEval: Evaluating Reasoning Chains via Correctness and InformativenessConference on Empirical Methods in Natural Language Processing (EMNLP)Archiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.2023 · 73 citations
12. Shepherd: A Critic for Language Model GenerationarXiv:2308.04592Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, et al.2023 · 60 citations
13. Instruction Pre-Training: Language Models are Supervised Multitask LearnersConference on Empirical Methods in Natural Language Processing (EMNLP)Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.2024 · 59 citations
14. Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Yongqi Tong, Dawei Li, Sizhe Wang, et al.2024 · 57 citations
15. Large Language Models are In-Context Semantic Reasoners rather than Symbolic ReasonersarXiv:2305.14825Xiaojuan Tang, Zilong Zheng, Jiaqi Li, et al.2023 · 52 citations
16. What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient PerspectiveAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Li, Yanhong Li, Tianyi Zhou2025 · 15 citations
17. FaithLM: Towards Faithful Explanations for Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, et al.2026 · 9 citations
18. ArabicSense: A Benchmark for Evaluating Commonsense Reasoning in Arabic with Large Language ModelsWorkshop on Arabic Corpus LinguisticsSalima Lamsiyah, Kamyar Zeinalipour, Samir El amrany, et al.2025 · 7 citations

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