Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes

Borji, Ali

doi:10.2139/ssrn.4402938

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…ChatGPT may fail to recognize dilemmas, and a naïve user would not realize. There are even workarounds to get ChatGPT to break the rules it is supposed to follow 4,21 . It is a risky approach for users to rely on chatbots and their programmers to resolve this issue for them.…”

Section: Discussionmentioning

confidence: 99%

ChatGPT’s inconsistent moral advice influences users’ judgment

Krügel

Ostermaier

Uhl

2023

Sci Rep

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ChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it can improve the moral judgment and decisions of users. Unfortunately, ChatGPT’s advice is not consistent. Nonetheless, it does influence users’ moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT corrupts rather than improves its users’ moral judgment. While these findings call for better design of ChatGPT and similar bots, we also propose training to improve users’ digital literacy as a remedy. Transparency, however, is not sufficient to enable the responsible use of AI.

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Section: Discussionmentioning

confidence: 99%

ChatGPT’s inconsistent moral advice influences users’ judgment

Krügel

Ostermaier

Uhl

2023

Sci Rep

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“…There are also some limitations and challenges to the use of ChatGPT for applications related to pharmacometrics. For example, the accuracy and reliability of AI-generated data may be affected by biases and knowledge gaps in the training data or the complexity of the query, for example when asking to produce code for more complex biological systems 21,22 . It can be especially difficult for inexperienced users to detect errors in the responses provided by ChatGPT, potentially with errors getting into proposed model code, affecting downstream results.…”

Section: Discussionmentioning

confidence: 99%

ChatGPT in pharmacometrics? Potential opportunities and limitations

Denejour¹

2023

Preprint

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The potential of using chatGPT in pharmacometrics was explored in this study, with a focus on developing a pharmacokinetic (PK) model for standard half-life factor VIII. Our results demonstrated that chatGPT can be utilized to accurately obtain typical PK parameters from literature, generate a population PK model in R, and develop an interactive Shiny application to visualize the results. ChatGPT’s language generation capabilities enabled the development of R codes with minimal programming knowledge and helped identify and fix errors in the code. While chatGPT presents several advantages, such as its ability to streamline the development process, its use in pharmacometrics also has limitations and challenges, including the accuracy and reliability of AI-generated data, the lack of transparency and interpretability of AI. Overall, our study demonstrates the potential of using chatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs.

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“…GPT-3 is able to perform competitively on several tasks such as questionanswering, semantic parsing (Shin and Van Durme 2022) and machine translation. However, such LLMs tend to make simple mistakes in tasks such as semantic (commonsense) and mathematical reasoning (Floridi and Chiriatti 2020;Wei et al 2022;Borji 2019).…”

Section: Large Language Modelsmentioning

confidence: 99%

“…The recently popular ChatGPT, with its surprising performance and powerful conversational ability, brought Large Language Models (LLMs) such as GPT-3 (Brown, Mann, and Others 2020), PaLM (Chowdhery et al 2022), and LLaMa (Touvron et al 2023) as the solution to the vexing problem of developing conversational AI systems. These LLMs work quite well in content generation tasks such as translation and creative writing, but their deficiency in fact-and-knowledge-oriented tasks is well-established by now (Borji 2019). These models themselves cannot tell whether the text they generate is based on facts or madeup stories, and they cannot always follow the given data and rules strictly and sometimes even modify the data at will.…”

Section: Introductionmentioning

confidence: 99%

“…The reason for the above issues is that LLMs generate text that is purely based on a pattern-matching mechanism. As a result, LLMs have absolutely no understanding of the meaning of sentences and thus lack awareness (Borji 2019). In contrast, humans understand the meaning of sentences, then use their reasoning capabilities to draw further conclusions, check for consistency, or determine missing information from this meaning.…”

Section: Introductionmentioning

confidence: 99%

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Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs

Zeng¹,

Rajasekharan²,

Padalkar³

et al. 2023

Preprint

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Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. While Large Language Models (LLMs) have shown promise in generating human-like sentences for tasks such as question answering, paragraph summarization, and translation, they rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. As a result, they may generate incorrect responses. To generate an assuredly correct response, one has to "understand" the semantics of a sentence, so that the missing information can be further requested and the correct response computed. To achieve this "understanding", logic-based (commonsense) reasoning methods such as Answer Set Programming (ASP) are arguably needed. In this paper, we describe the AutoConcierge system that leverages LLMs and ASP to develop a conversational agent that can truly "understand" human dialogs, at least in restricted domains. AutoConcierge is focused on a specific domain-advising users about restaurants in their local area based on their preferences. AutoConcierge will interactively understand a user's utterances, identify the missing information in them, and request the user via a natural language sentence to provide it. Once AutoConcierge has determined that all the information has been received, it computes a restaurant recommendation based on the user-preferences it has acquired from the human user. AutoConcierge is based on our STAR framework developed earlier, which uses GPT-3 to convert human dialogs into predicates that capture the deep structure of the dialog's sentence. These predicates are then input into the goal-directed s(CASP) ASP system for performing commonsense reasoning. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.

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Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes

Cited by 3 publications

References 22 publications

ChatGPT’s inconsistent moral advice influences users’ judgment

ChatGPT’s inconsistent moral advice influences users’ judgment

ChatGPT in pharmacometrics? Potential opportunities and limitations

Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs

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