---
title: "Srinivasan Iyer — all works — Elamur Research"
description: "All 27 works of Srinivasan Iyer in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
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---
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# Srinivasan Iyer — all works

27 works · [← Srinivasan Iyer](https://md.research.elamur.ai/free/en/authors/srinivasan-iyer/)

1. **[LIMA: Less Is More for Alignment ↗](https://arxiv.org/abs/2305.11206) arXiv:2305.11206 · no summary yet**Chunting Zhou et al.*2023 · 1,022 citations*
2. **[Summarizing Source Code using a Neural Attention Model ↗](https://aclanthology.org/P16-1195/) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Srinivasan Iyer et al.*2016 · 800 citations*
3. **[Learning a Neural Semantic Parser from User Feedback ↗](https://aclanthology.org/P17-1089/) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Srinivasan Iyer et al.*2017 · 472 citations*
4. [**Neural AMR: Sequence-to-Sequence Models for Parsing and Generation**Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, et al.*Machine translation · 425 citations*](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/)
5. **[Mapping Language to Code in Programmatic Context ↗](https://aclanthology.org/D18-1192/) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yet**Srinivasan Iyer et al.*2018 · 260 citations*
6. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization arXiv:2212.12017**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
7. **[Demystifying Prompts in Language Models via Perplexity Estimation ↗](https://arxiv.org/abs/2212.04037) arXiv:2212.04037 · no summary yet**Hila Gonen et al.*2022 · 213 citations*
8. **[Efficient One-Pass End-to-End Entity Linking for Questions ↗](https://aclanthology.org/2020.emnlp-main.522/) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yet**Belinda Z. Li et al.*2020 · 172 citations*
9. **[Learning to Map Context-Dependent Sentences to Executable Formal Queries ↗](https://aclanthology.org/N18-1203/) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · no summary yet**Alane Suhr, Srinivasan Iyer, Yoav Artzi*2018 · 139 citations*
10. **[Efficient Large Scale Language Modeling with Mixtures of Experts ↗](https://arxiv.org/abs/2112.10684) arXiv:2112.10684 · no summary yet**Mikel Artetxe et al.*2021 · 113 citations*
11. **[Complementary Explanations for Effective In-Context Learning](https://md.research.elamur.ai/free/en/papers/ye-2023-complementary-explanations-effective-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.*2023 · 106 citations*
12. **[JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation](https://md.research.elamur.ai/free/en/papers/agashe-2019-juice-large-scale-distantly-supervised/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer*2019 · 75 citations*
13. **[Byte Latent Transformer: Patches Scale Better Than Tokens ↗](https://aclanthology.org/2025.acl-long.453/) Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Artidoro Pagnoni et al.*2025 · 73 citations*
14. **[DeLighT: Deep and Light-weight Transformer ↗](https://arxiv.org/abs/2008.00623) arXiv (Cornell University) · no summary yet**Sachin Mehta et al.*2020 · 64 citations*
15. **[Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs ↗](https://arxiv.org/abs/2111.13654) arXiv:2111.13654 · no summary yet**Peter Hase et al.*2021 · 60 citations*
16. **[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*
17. **[Learning Programmatic Idioms for Scalable Semantic Parsing ↗](https://aclanthology.org/D19-1545/) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yet**Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer*2019 · 48 citations*
18. **[Instruction-tuned Language Models are Better Knowledge Learners ↗](https://aclanthology.org/2024.acl-long.296/) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Zhengbao Jiang et al.*2024 · 47 citations*
19. **[Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models ↗](https://arxiv.org/abs/2411.04996) arXiv:2411.04996 · Transactions on Machine Learning Research (2025), ISSN: 2835-8856 · no summary yet**Weixin Liang et al.*2024 · 25 citations*
20. **[Neural Semantic Parsing ↗](https://aclanthology.org/P18-5006/) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts · no summary yet**Matt Gardner et al.*2018 · 23 citations*
21. **[MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts ↗](https://arxiv.org/abs/2407.21770) arXiv (Cornell University) · no summary yet**Xi Lin et al.*2024 · 21 citations*
22. **[ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization ↗](https://aclanthology.org/2024.findings-acl.597/) Findings of the Association for Computational Linguistics: ACL 2024 · no summary yet**David Wan et al.*2024 · 20 citations*
23. **[Scaling Test-Time Compute for Agentic Coding ↗](https://arxiv.org/abs/2604.16529) arXiv (Cornell University) · no summary yet**Joongwon Kim et al.*2026 · 3 citations*
24. **[Fast Byte Latent Transformer ↗](https://arxiv.org/abs/2605.08044) arXiv (Cornell University) · no summary yet**Julie Kallini et al.*2026 · 1 citation*
25. **[Compute Optimal Tokenization ↗](https://arxiv.org/abs/2605.01188) arXiv (Cornell University) · no summary yet**Tomasz Limisiewicz et al.*2026 · 1 citation*
26. **[Latent Speech-Text Transformer ↗](https://arxiv.org/abs/2510.06195) arXiv (Cornell University) · no summary yet**Yen-Ju Lu et al.*2025 · 1 citation*
27. **[Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models ↗](https://arxiv.org/abs/2609.12303) arXiv (Cornell University) · no summary yet**Kalyani Marathe et al.*2026*