Siva Reddy
Research Scientist
AI Research Partnerships & Ecosystem
Siva Reddy is Assistant Professor in the School of Computer Science and the Department of Linguistics at McGill University, a member of Mila, and a Research Scientist at ServiceNow AI Research. His area of research is Natural Language Processing, and his research goal is to enable machines with language understanding abilities such that conversing with them feel as natural as conversing with humans. Along this process, Siva hopes to discover fundamental representations of language, both symbolic and distributional, that allow us to study the connection between language and meaning. His expertise includes building symbolic and deep learning models for language understanding. He works on problems such as semantic parsing, question answering, reading comprehension and conversational systems.
Interests
- Natural Language Processing
- Retrieval
- Dialog
- Natural Language Understanding
- Trustworthiness
- Large Language Models
- Human Machine Interaction Through Language
- Multi-lingual
- Semantic Parsing
Publications
WebArena-Pro: A Heterogeneous, Multimodal, Reproducible Benchmark for Web Agents.
Workshop at the International Conference of Machine Learning (ICML),
2026.
Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency.
International Conference on Machine Learning (ICML),
2026.
Societal Alignment Frameworks Can Improve LLM Alignment.
ACM Conference on Fairness, Accountability, and Transparency,
2026.
Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency.
Workshop at the International Conference of Machine Learning (ICML),
2026.
The Promise of RL for Autoregressive Image Editing.
Neural Information Processing Systems (NeurIPS),
2025.
Understanding the Influence of Synthetic Data for Text Embedders.
Annual Meeting of the Association for Computational Linguistics (ACL),
2025.
SafeArena: Evaluating the Safety of Autonomous Web Agents.
International Conference on Machine Learning (ICML),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Workshop at the Computer Vision and Pattern Recognition Conference (CVPR),
2025.
Societal Alignment Frameworks Can Improve LLM Alignment.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks.
International Conference of Learning Representations (ICLR),
2025.
MMTEB: Massive Multilingual Text Embedding Benchmark.
International Conference of Learning Representations (ICLR),
2025.
The BrowserGym Ecosystem for Web Agent Research.
Transactions on Machine Learning Research (TMLR),
2025.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.
Conference on Language Modeling (COLM),
2024.
Evaluating In-Context Learning of Libraries for Code Generation.
North American Chapter of the Association for Computational Linguistics (NAACL),
2024.
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering.
Transactions of the Association for Computational Linguistics (TACL),
2024.
MAGNIFICO: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2023.
Are Diffusion Models Vision-And-Language Reasoners?.
Conference on Neural Information Processing Systems (NeurIPS),
2023.
StarCoder: may the source be with you!.
Transactions on Machine Learning Research (TMLR),
2023.
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents.
European Chapter of the Association for Computational Linguistics (EACL),
2023.
In-Context Learning for Text Classification with Many Labels.
Workshop at the Conference on Empirical Methods in Natural Language Processing (EMNLP),
2022.
Does entity abstraction help generative Transformers reason? .
Transactions on Machine Learning Research,
2022.
Compositional Generalization in Dependency Parsing.
Annual Meeting of the Association for Computational Linguistics (ACL),
2022.
The Power of Prompt Tuning for Low-Resource Semantic Parsing.
Annual Meeting of the Association for Computational Linguistics (ACL),
2022.
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching.
Transactions of the Association for Computational Linguistics (TACL),
2021.
Understanding by Understanding Not: Modeling Negation in Language Models.
North American Chapter of the Association for Computational Linguistics (NAACL),
2021.
Measuring Systematic Generalization in Neural Proof Generation with Transformers.
Conference on Neural Information Processing Systems (NeurIPS),
2020.