Zelikman, Eric

Holistic Evaluation of Language Models

Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 255

STaR: Bootstrapping Reasoning With Reasoning

Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022) · arXiv

Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

Cited by 182

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Kreiss, E., Bennett, C., Hooshmand, S., Zelikman, E., Ringel Morris, M., & Potts, C. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsDigital Accessibility for Disabilities

Cited by 24

Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Zelikman, E., Harik, G., Shao, Y., Jayasiri, V., Haber, N., & Goodman, N. D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 16

Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions

Zelikman, E., Huang, Q., Poesia, G., Goodman, N., & Haber, N. (2023) · Advances in Neural Information Processing Systems 36

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

Cited by 15

Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions

Zelikman, E., Huang, Q., Poesia, G., Goodman, N. D., & Haber, N. (2022) · arXiv

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

Cited by 15

Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

Zelikman, E., Lorch, E., Mackey, L., & Kalai, A. T. (2023) · arXiv

Software Engineering ResearchEvolutionary Algorithms and Applications

Cited by 4