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Could you please demonstrate your claim by querying the GPT4 model to respond to my above comment and providing the reply? Let's see what the 'expert' has to sa

Could you please demonstrate your claim by querying the GPT4 model to respond to my above comment and providing the reply? Let's see what the 'expert' has to say about it.

MacsHeadroom 4y ago
I don't understand what you want me to ask it about. So I asked it something, as usual every citation and URL is real despite not having internet access or training data after 2021: <PROMPT>List some citations of recent Machine Learning papers discussing reasoning ability in Machine Learning Models such as HellaSwag. [REPLY]" As of my knowledge cutoff in September 2021, I can provide you with some citations for ML papers that discuss reasoning abilities in models and datasets like HellaSwag. However, you may want to search for more recent papers using a platform like arXiv or Google Scholar. Here are a few relevant papers: 2019 HellaSwag: Can a Machine Really Finish Your Sentence? 2019 COMET: Commonsense Transformers for Automatic Knowledge Graph Construction. 2019 From ‘F’ to ‘A’ on the N.Y. Regents Science Exams: An Overview of the Aristo Project. 2020 A Primer in BERTology: What We Know About How BERT Works. To find more recent papers on this topic, I recommend searching for keywords like "reasoning," "machine learning," "natural language understanding," "commonsense reasoning," and "HellaSwag" on academic paper databases like arXiv, ACL Anthology, or Google Scholar." <PROMPT>In 50 words or less, what do these papers tell us about emergent capabilities of LLMs? [REPLY]" These papers explore the capabilities of large language models (LLMs) in understanding context, generating coherent text, and performing commonsense reasoning. They investigate the underlying mechanisms, evaluate their performance on challenging benchmarks, and propose methods to improve LLMs' ability to reason and construct knowledge graphs from natural language input." <PROMPT>Please list the URLS to the arxiv versions of the papers you cited. [REPLY]" I can provide you with the URLs to the arXiv versions of the papers I cited earlier: HellaSwag: Can a Machine Really Finish Your Sentence? URL: https://arxiv.org/abs/1905.07830 https://arxiv.org/abs/1905.07830 COMET: Commonsense Transformers for Automatic Knowledge Graph Construction URL: https://arxiv.org/abs/1906.05317 https://arxiv.org/abs/1906.05317 From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project URL: https://arxiv.org/abs/1909.01958 https://arxiv.org/abs/1909.01958 A Primer in BERTology: What We Know About How BERT Works URL: https://arxiv.org/abs/2002.12327 https://arxiv.org/abs/2002.12327 Please note that these papers were published before my knowledge cutoff date, and more recent research might be available. To find the latest research on the topic, I recommend searching on arXiv or Google Scholar using relevant keywords." ================ Edit: There's more: <PROMPT>A hacker news commenter thinks LLMs are incapable of comprehension. Give an argument for why he is wrong. [REPLY]" Massive LLMs demonstrate a strong ability to "comprehend" text by capturing complex language patterns and associations in their training data. While their comprehension may not be identical to human-level understanding, they can still generate contextually relevant responses, answer questions, and perform tasks that require reasoning. Their performance on various benchmarks, including natural language understanding and commonsense reasoning tasks, indicates a level of comprehension that goes beyond simple pattern matching." <PROMPT>What citations can back up you argument, omit authors, list title and year only. [REPLY]" 1. "HellaSwag: Can a Machine Really Finish Your Sentence?", 2019 2. "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction", 2019 3. "From ‘F’ to ‘A’ on the N.Y. Regents Science Exams: An Overview of the Aristo Project", 2020 4. "A Primer in BERTology: What We Know About How BERT Works", 2020 5. "Language Models are Few-Shot Learners", 2020" <PROMPT>Tell me about Language Models are Few-Shot Learners [REPLY]" "Language Models are Few-Shot Learners" is a paper published in 2020, which introduces GPT-3, the third iteration of OpenAI's Generative Pre-trained Transformer. GPT-3 is a state-of-the-art large-scale language model consisting of 175 billion parameters, significantly larger than its predecessor, GPT-2. The paper primarily investigates the few-shot learning capabilities of GPT-3. Few-shot learning refers to a model's ability to generalize and adapt to new tasks with limited examples or training data. The authors demonstrate that GPT-3 can perform a wide range of tasks, including translation, summarization, question-answering, and even arithmetic, by simply conditioning the model on a few examples provided in natural language. This research highlights the potential of scaling up language models to improve their performance across multiple natural language understanding and generation tasks. GPT-3's impressive capabilities showcase that large language models can effectively learn from limited examples and generalize their knowledge to perform a diverse set of tasks without task-specific fine-tuning."
- HybridCurve 4y ago
  Yes, If you ask something specific it will generate citations because there are might be strong positive associations between that topic and research. This is a good example of a proper query. But for instance, if you ask it about "citations for papers on recent research on trees" for instance you get a bunch of nonsense. In response to my earlier comment that it was "an expert in nothing", this was the reply I received from: ME: Can you respond to this statement: "<my earlier comment>" ChatGPT: The statement that "ChatGPT is an expert in nothing" is technically true, as I am a machine learning model that has been trained on a vast corpus of data and does not possess personal expertise or experience. However, my purpose is to provide information to the best of my ability based on my training data, which allows me to offer a wide range of information on various topics. It is true that the information produced by me or any other AI language model should be cross-checked for accuracy, as errors and inaccuracies may occur. It is also true that I do not possess a conscious understanding of the world in the way that humans do, and my responses are generated through statistical analysis of patterns in my training data. Therefore, while I can provide accurate and useful information, my responses are not always nuanced or contextualized in the way that a human expert might be able to provide. So either it is agreeing with me and my statement was correct, or it's reply was incorrect and therefore my statement was correct. Am I wrong on this? Does whatever model you have access to provide an alternate take? Edit: For the above, you should start a new conversation to clear the existing context. The citations appear to have just been repeated.
  - MacsHeadroom 4y ago
    >So either it is agreeing with me and my statement was correct, or it's reply was incorrect and therefore my statement was correct. Am I wrong on this? Yes you're wrong for admitting to having an unfalsifiable belief about this. If your belief can't be falsified then this discussion is pointless. The model I have access to scores higher than most humans at complex abstract reasoning, for what it's worth (which is apparently nothing because regardless of any outcomes you will declare yourself correct).
    - mrguyorama 4y ago

Could you please demonstrate your claim by querying the GPT4 model to respond to my above comment and providing the reply? Let's see what the 'expert' has to say about it.

MacsHeadroom 4y ago

I don't understand what you want me to ask it about. So I asked it something, as usual every citation and URL is real despite not having internet access or training data after 2021: <PROMPT>List some citations of recent Machine Learning papers discussing reasoning ability in Machine Learning Models such as HellaSwag. [REPLY]" As of my knowledge cutoff in September 2021, I can provide you with some citations for ML papers that discuss reasoning abilities in models and datasets like HellaSwag. However, you may want to search for more recent papers using a platform like arXiv or Google Scholar. Here are a few relevant papers: 2019 HellaSwag: Can a Machine Really Finish Your Sentence? 2019 COMET: Commonsense Transformers for Automatic Knowledge Graph Construction. 2019 From ‘F’ to ‘A’ on the N.Y. Regents Science Exams: An Overview of the Aristo Project. 2020 A Primer in BERTology: What We Know About How BERT Works. To find more recent papers on this topic, I recommend searching for keywords like "reasoning," "machine learning," "natural language understanding," "commonsense reasoning," and "HellaSwag" on academic paper databases like arXiv, ACL Anthology, or Google Scholar." <PROMPT>In 50 words or less, what do these papers tell us about emergent capabilities of LLMs? [REPLY]" These papers explore the capabilities of large language models (LLMs) in understanding context, generating coherent text, and performing commonsense reasoning. They investigate the underlying mechanisms, evaluate their performance on challenging benchmarks, and propose methods to improve LLMs' ability to reason and construct knowledge graphs from natural language input." <PROMPT>Please list the URLS to the arxiv versions of the papers you cited. [REPLY]" I can provide you with the URLs to the arXiv versions of the papers I cited earlier: HellaSwag: Can a Machine Really Finish Your Sentence? URL: https://arxiv.org/abs/1905.07830 https://arxiv.org/abs/1905.07830 COMET: Commonsense Transformers for Automatic Knowledge Graph Construction URL: https://arxiv.org/abs/1906.05317 https://arxiv.org/abs/1906.05317 From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project URL: https://arxiv.org/abs/1909.01958 https://arxiv.org/abs/1909.01958 A Primer in BERTology: What We Know About How BERT Works URL: https://arxiv.org/abs/2002.12327 https://arxiv.org/abs/2002.12327 Please note that these papers were published before my knowledge cutoff date, and more recent research might be available. To find the latest research on the topic, I recommend searching on arXiv or Google Scholar using relevant keywords." ================ Edit: There's more: <PROMPT>A hacker news commenter thinks LLMs are incapable of comprehension. Give an argument for why he is wrong. [REPLY]" Massive LLMs demonstrate a strong ability to "comprehend" text by capturing complex language patterns and associations in their training data. While their comprehension may not be identical to human-level understanding, they can still generate contextually relevant responses, answer questions, and perform tasks that require reasoning. Their performance on various benchmarks, including natural language understanding and commonsense reasoning tasks, indicates a level of comprehension that goes beyond simple pattern matching." <PROMPT>What citations can back up you argument, omit authors, list title and year only. [REPLY]" 1. "HellaSwag: Can a Machine Really Finish Your Sentence?", 2019 2. "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction", 2019 3. "From ‘F’ to ‘A’ on the N.Y. Regents Science Exams: An Overview of the Aristo Project", 2020 4. "A Primer in BERTology: What We Know About How BERT Works", 2020 5. "Language Models are Few-Shot Learners", 2020" <PROMPT>Tell me about Language Models are Few-Shot Learners [REPLY]" "Language Models are Few-Shot Learners" is a paper published in 2020, which introduces GPT-3, the third iteration of OpenAI's Generative Pre-trained Transformer. GPT-3 is a state-of-the-art large-scale language model consisting of 175 billion parameters, significantly larger than its predecessor, GPT-2. The paper primarily investigates the few-shot learning capabilities of GPT-3. Few-shot learning refers to a model's ability to generalize and adapt to new tasks with limited examples or training data. The authors demonstrate that GPT-3 can perform a wide range of tasks, including translation, summarization, question-answering, and even arithmetic, by simply conditioning the model on a few examples provided in natural language. This research highlights the potential of scaling up language models to improve their performance across multiple natural language understanding and generation tasks. GPT-3's impressive capabilities showcase that large language models can effectively learn from limited examples and generalize their knowledge to perform a diverse set of tasks without task-specific fine-tuning."

HybridCurve 4y ago
Yes, If you ask something specific it will generate citations because there are might be strong positive associations between that topic and research. This is a good example of a proper query. But for instance, if you ask it about "citations for papers on recent research on trees" for instance you get a bunch of nonsense. In response to my earlier comment that it was "an expert in nothing", this was the reply I received from: ME: Can you respond to this statement: "<my earlier comment>" ChatGPT: The statement that "ChatGPT is an expert in nothing" is technically true, as I am a machine learning model that has been trained on a vast corpus of data and does not possess personal expertise or experience. However, my purpose is to provide information to the best of my ability based on my training data, which allows me to offer a wide range of information on various topics. It is true that the information produced by me or any other AI language model should be cross-checked for accuracy, as errors and inaccuracies may occur. It is also true that I do not possess a conscious understanding of the world in the way that humans do, and my responses are generated through statistical analysis of patterns in my training data. Therefore, while I can provide accurate and useful information, my responses are not always nuanced or contextualized in the way that a human expert might be able to provide. So either it is agreeing with me and my statement was correct, or it's reply was incorrect and therefore my statement was correct. Am I wrong on this? Does whatever model you have access to provide an alternate take? Edit: For the above, you should start a new conversation to clear the existing context. The citations appear to have just been repeated.
- MacsHeadroom 4y ago
  >So either it is agreeing with me and my statement was correct, or it's reply was incorrect and therefore my statement was correct. Am I wrong on this? Yes you're wrong for admitting to having an unfalsifiable belief about this. If your belief can't be falsified then this discussion is pointless. The model I have access to scores higher than most humans at complex abstract reasoning, for what it's worth (which is apparently nothing because regardless of any outcomes you will declare yourself correct).
  - mrguyorama 4y ago