NeurIPS 2023: A Technical Summary

The 37th Neural Information Processing Systems (NeurIPS) conference saw a record-breaking 3586 paper submissions accepted for presentation in its main track, with interest in large language models (LLMs) dominating discussions. Researchers explored various aspects of LLMs, including their planning capabilities, pretraining and fine-tuning methods, and applications. Papers presented at the conference addressed topics such as optimistic exploration in reinforcement learning using symbolic model estimates, localization versus knowledge editing in language models, and the planning abilities of large language models. Additionally, researchers discussed efficient finetuning approaches for quantized LLMs, training language models to use external tools, and explored the complexities of scaling LLMs for end-users. The conference also featured a panel on "LLMs: Beyond Scaling", which highlighted debates around proprietary versus open-source models and the importance of discussing research results in a venue like NeurIPS.

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