14 citations · 14 across the 3 of their papers we have counts for
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cs.CL2024★ 14 cited
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Ke Yang, Jiateng Liu, John Wu +9
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural langua…
cs.CL2023
Defining a New NLP Playground
Sha Li, Chi Han, Pengfei Yu +8
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in…
cs.CL2023
Making Pre-trained Language Models both Task-solvers and Self-calibrators
Yangyi Chen, Xingyao Wang, Heng Ji
Pre-trained language models (PLMs) serve as backbones for various real-world systems. For high-stake applications, it's equally essential to have reasonable confidence estimations…