A Survey on the Real Power of ChatGPT
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Abstract (arXiv)
ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source and traditional benchmark datasets may have been used by ChatGPT as the training data. In this paper, (i) we survey recent studies which uncover the real performance levels of ChatGPT in seven categories of NLP tasks, (ii) review the social implications and safety issues of ChatGPT, and (iii) emphasize key challenges and opportunities for its evaluation. We hope our survey can shed some light on its blackbox manner, so that researchers are not misleaded by its surface generation.
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References · 50
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
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- 2023Large Language Modelscited by 774
- 2022Towards Reasoning in Large Language Models: A Surveycited by 593
- 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
- 2020AdapterHub: A Framework for Adapting Transformerscited by 441
- 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
- 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
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38 more
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- 2023Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answeringcited by 28
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- 2023Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPTcited by 16
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- 2023Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?cited by 12
- 2023CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Codecited by 11
- 2023ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitationscited by 9
- 2023Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspapercited by 9
- 2023UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Predictioncited by 8
- 2023Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparisoncited by 6
- 2022What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitionscited by 5
- 2023Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluationcited by 5
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