Paper Digest: NeurIPS-2023 Highlights (Full List)

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• Paper Digest: NeurIPS-2023 Highlights (Full List)

Paper Digest: NeurIPS 2023 Highlights

https://www.paperdigest.org

1, Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick; Jane Dwivedi-Yu; Roberto Dessi; Roberta Raileanu; Maria Lomeli; Eric Hambro; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom;
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Highlight: In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds.

2, Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; Katherine Hermann; Sean Welleck; Amir Yazdanbakhsh; Peter Clark;
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Highlight: Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement.

3, Vicuna Evaluation: Exploring LLM-as-a-Judge and Chatbot Arena
Lianmin Zheng; Wei-Lin Chiang; Ying Sheng; Siyuan Zhuang; Zhanghao Wu; Yonghao Zhuang; Zi Lin; Zhuohan Li; Dacheng Li; Eric Xing; Hao Zhang; Joseph Gonzalez; Ion Stoica;
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Highlight: To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, including position, verbosity, and self-enhancement biases, as well as limited reasoning ability, and propose solutions to mitigate some of them.

Suggested labels

{ "key": "LLM-Applications", "value": "Topics related to practical applications of Large Language Models in various fields" }