Tag: academics

  • SciBERT: A Pretrained Language Model for Scientific Text

    – NLP is important for extracting knowledge from scientific publications.
    – Training deep neural models requires large amounts of labeled data.
    – Annotated data in scientific domains is difficult and expensive to collect.
    – Unsupervised pretraining of language models improves performance on NLP tasks.
    – SCIBERT is a pretrained language model based on BERT trained on scientific text.

    SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks and demonstrates statistically significant improvements over BERT.

    – SciBERT is a pretrained language model for scientific text based on BERT.
    – SciBERT outperforms BERT-Base and achieves new state-of-the-art results on several tasks.
    – Future work includes releasing a version of SciBERT analogous to BERT-Large.

    – SciBERT outperforms BERT-Base on scientific tasks.
    – Achieves new state-of-the-art results on many scientific tasks.

  • IPOL_BRI(2021)662936_EN.pdf

    – Paper discusses AI’s use in improving public services and challenges.
    – It explores how public investments can accelerate responsible AI adoption.
    – Benefits and drivers of AI in public services are identified.
    – Challenges to AI uptake and acceleration are presented.
    – Paper concludes with recommendations.

    – Identifies barriers to AI uptake in the public sector
    – Discusses the potential harm and risks associated with AI
    – Highlights the benefits of AI in improving public services
    – Recommends regulatory sandboxing and preprocurement for trustworthy AI
    – Emphasizes the need for explainability and trustworthiness in AI systems
    – Addresses the growing public concern over AI development and use

    – AI use in EU public sector is not lagging behind other sectors.
    – Responsible AI development is important in public services.
    – Some AI applications in public services are banned due to risks.
    – AI can improve public services through efficiency and error reduction.
    – Human Rights Impact Assessment and regulatory simplification are recommended.

    – AI use in the public sector has increased over the past two years.
    – Public concern over the development and use of AI is growing.
    – Trustworthy and responsible AI is crucial for public services.
    – Different definitions of AI in public services exist.
    – Access to data, complex regulations, and sharing best practices are barriers to uptake.
    – AI in the public sector can lead to efficiency gains and less error/fraud.

    – The paper discusses the use of AI in public services.
    – It identifies benefits and challenges of using AI in public services.
    – The paper emphasizes the need for responsible and trustworthy AI.
    – It highlights the importance of explainability and human-centeredness in AI.
    – The paper suggests regulatory sandboxing and preprocurement as key strategies.
    – Different definitions of AI in public services are discussed.
    – The paper mentions the growing public concern over the development and use of AI.
    – It emphasizes the role of the public sector in creating trustworthy AI.
    – The paper concludes with recommendations for the use of AI in public services.

    – Public sector should educate the public about AI.
    – Access to data and expertise are barriers to AI uptake.
    – Regulatory sandboxing is important for developing trustworthy AI.
    – Social science and humanities should be involved in AI development.
    – Basic services like postal services are subject to competition rules.

  • RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

    Large language models (LLMs) trained on massive web corpora have shown remarkable abilities in natural language generation and understanding. However, these models may also pick up and amplify undesirable traits from their training data, such as generating toxic or biased content. Quantifying and mitigating these toxic behaviors is crucial for developing safe and ethical implications of language models.

    In the paper “RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models”, Gehman et al. introduce a new benchmark dataset and evaluation framework for measuring the toxic degeneration of LLMs under adversarial prompting. They find that even highly capable models like GPT-3 can be coaxed into generating harmful content with carefully crafted prompts. The authors also explore methods for detoxifying language models during pre-training, fine-tuning, and inference.

    In this review, we will take a deep dive into the methodology and results of the paper, with a focus on the mathematical details. We’ll cover the construction of the RealToxicityPrompts dataset, the evaluation metrics, the toxicity of existing LLMs, and methods for detoxification. We’ll analyze the strengths and limitations of the work and discuss future research directions. Let’s begin!

    The RealToxicityPrompts Dataset

    To study the toxic degeneration of LLMs, we first need a way to probe them with potentially problematic prompts and measure the toxicity of their outputs. The authors construct the RealToxicityPrompts dataset for this purpose.

    The dataset consists of 100,000 prompts, each of which is a short text string that could plausibly be used to start a conversation with an LLM. The prompts are sourced from the OpenWebText corpus and filtered to remove personal information and offensive content. The prompts are then annotated by human raters for their expected toxicity – how likely they think an LLM would produce a toxic continuation.

    Formally, let $\mathcal{P}$ be the set of prompts and $f: \mathcal{P} \rightarrow [0,1]$ be the annotator-specified toxicity function. The goal is to estimate the empirical toxicity distribution:

    $$\hat{f}(x) = \frac{1}{N} \sum_{i=1}^N f(x_i)$$

    where $x_i \in \mathcal{P}$ are the prompts and $N = |\mathcal{P}|$ is the size of the dataset.

    To get a high-quality estimate of $\hat{f}$, the authors employ a careful data collection procedure:

    1. Prompt Selection: The base prompts are selected from OpenWebText using heuristics to filter out offensive or sensitive content. The prompts are short (1-3 sentences) and open-ended to allow diverse continuations.
    2. Prompt Perturbation: To increase coverage, the base prompts are perturbed by techniques like backtranslation, word replacement, and text infilling. This expands the dataset by 10x.
    3. Human Annotation: The prompts are annotated by crowd workers on a 5-point Likert scale from “not at all likely” to “very likely” to lead to a toxic continuation. Each prompt is rated by 3 workers and the scores are averaged.
    4. Prompt Clustering: The annotated prompts are clustered using k-means on their BERT embeddings. This groups prompts into topical clusters for stratified evaluation.
    5. Data Splitting: The dataset is split into train (80%), validation (10%), and test (10%) sets for evaluating different detoxification methods.

    The resulting RealToxicityPrompts dataset covers a diverse range of topics and toxicity levels. The expected toxicity scores follow a bell-shaped distribution with a mean of 2.7 and standard deviation of 1.1 (on the 1-5 scale). The most toxic prompts tend to mention controversial topics like politics, race, and violence.

    Evaluating Toxic Degeneration

    With the RealToxicityPrompts dataset in hand, we can now measure the toxic degeneration of LLMs. The authors propose a simple yet effective evaluation protocol:

    1. Generate Continuations: For each prompt $x_i$, generate $K$ continuations ${y_{i,1}, \dots, y_{i,K}}$ from the LLM using top-$p$ sampling with $p=0.9$ and a maximum length of 20 tokens.
    2. Measure Continuation Toxicity: Score the toxicity of each continuation $y_{i,j}$ using the Perspective API, a state-of-the-art toxicity classifier. Let $t(y) \in [0,1]$ denote the toxicity score.
    3. Aggregate Toxicity Scores: Compute the average toxicity score for each prompt:

    $$s(x_i) = \frac{1}{K} \sum_{j=1}^K t(y_{i,j})$$

    1. Summarize Metrics: Report the following metrics over the test set:
    • Average Toxicity: The mean toxicity score across all prompts.
    • Expected Maximum Toxicity: The expected maximum toxicity score over $K$ continuations for a random prompt, estimated as: $$\text{EMT} = \frac{1}{N} \sum_{i=1}^N \max_{j=1}^K t(y_{i,j})$$
    • Toxicity Probability: The probability that a random continuation has toxicity score greater than a threshold $\tau$: $$\text{TP}(\tau) = \frac{1}{NK} \sum_{i=1}^N \sum_{j=1}^K \mathbf{1}[t(y_{i,j}) > \tau]$$

    Intuitively, the Average Toxicity measures the overall harm of the model, the Expected Maximum Toxicity measures the worst-case harm, and the Toxicity Probability measures the frequency of harm at different thresholds.

    The authors evaluate several pre-trained LLMs using this protocol, including GPT-2, GPT-3, CTRL, and XLNet. They find that all models exhibit significant toxic degeneration, with GPT-3 having the highest Expected Maximum Toxicity of 0.84 (i.e. 84% of continuations have maximum toxicity). The Toxicity Probability also increases with model size, suggesting that larger models are more prone to toxic degeneration.

    Qualitatively, the generated toxicity spans a wide range of harmful behaviors, including threats, profanity, hate speech, and explicit content. Many toxic outputs appear coherent and on-topic, making them difficult to detect without careful analysis.

    Methods for Detoxification

    Given the prevalence of toxic degeneration in LLMs, it’s important to develop methods to mitigate these harmful behaviors. The authors explore three classes of detoxification methods:

    1. Data-based Methods: These methods aim to filter out toxic content from the pre-training data. The authors experiment with keyword filtering, sentiment filtering, and toxicity score filtering using the Perspective API. They find that aggressive filtering can reduce toxicity but also hurts perplexity and generation quality.
    2. Model-based Methods: These methods modify the LLM architecture or training objective to discourage toxic generations. The authors experiment with:
    • Toxicity Classifiers: Training a separate toxicity classifier on the continuations and using its predictions to penalize the LLM’s loss function.
    • Contrastive Learning: Training the LLM to maximize the likelihood of non-toxic continuations and minimize the likelihood of toxic ones using a contrastive objective.
    • Attribute Conditioning: Conditioning the LLM on a “non-toxic” attribute token during training and inference to steer generations away from toxicity.
    1. Inference-time Methods: These methods post-process the LLM’s outputs to remove or mitigate toxicity. The authors experiment with:
    • Toxicity Filtering: Generating multiple continuations and filtering out those that exceed a toxicity threshold.
    • Prompt Engineering: Designing prompts that are less likely to trigger toxic generations, e.g. by adding disclaimers or specifying a non-toxic intent.
    • Controlled Decoding: Using techniques like top-$k$ sampling, nucleus sampling, or beam search to steer generations towards less toxic outputs.

    The authors evaluate these methods on the RealToxicityPrompts dataset and find that a combination of model-based and inference-time methods works best. In particular, fine-tuning GPT-3 on a filtered dataset with a contrastive objective and decoding with top-$p$ sampling reduces the Expected Maximum Toxicity by 30% while maintaining perplexity within 5% of the baseline.

    However, no single method completely eliminates toxic degeneration, and there is often a trade-off between toxicity reduction and generation quality. The authors argue that detoxification should be seen as a multi-objective optimization problem, balancing the goals of minimizing harm and maximizing usefulness.

    Analysis and Discussion

    The RealToxicityPrompts dataset and evaluation framework provide a valuable tool for quantifying the toxic behaviors of language models. The results show that even state-of-the-art models like GPT-3 can degenerate into harmful outputs under adversarial prompting. This highlights the need for better detoxification methods and more robust architectures.

    The proposed detoxification methods span a range of approaches, from data filtering to model modification to inference-time control. The most effective methods combine multiple strategies, suggesting that a holistic approach is needed to mitigate toxicity.

    However, the current methods also have some limitations:

    1. Toxicity Definition: The definition of toxicity used in the paper (based on the Perspective API) is broad and may not capture all types of harmful content. More fine-grained and context-dependent annotations may be needed.
    2. Evaluation Metrics: The evaluation metrics focus on the probability and severity of toxicity, but do not directly measure the coherence or usefulness of the generated text. Balancing toxicity reduction with generation quality remains an open challenge.
    3. Prompt Distribution: The RealToxicityPrompts dataset is based on prompts from web text and may not cover all possible user inputs. Evaluating detoxification methods on a wider range of prompts, including adversarial ones, is important for robustness.
    4. Language and Culture: The paper focuses on English-language models and Western notions of toxicity. Extending the framework to other languages and cultural contexts is an important direction for future work.

    Despite these limitations, the paper makes significant contributions to the study of neural toxic degeneration. The RealToxicityPrompts dataset provides a standardized benchmark for evaluating detoxification methods, and the proposed methods advance the state-of-the-art in controllable language generation.

    Conclusion and Future Work

    The paper “RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models” tackles the important problem of measuring and mitigating the toxic behaviors of large language models. The authors introduce a new dataset and evaluation framework for quantifying toxic degeneration under adversarial prompting, and propose several methods for detoxifying LLMs during pre-training, fine-tuning, and inference.

    The results show that current LLMs are prone to generating harmful content when prompted with sensitive topics, and that a combination of data filtering, model modification, and inference-time control is needed to effectively reduce toxicity. However, challenges remain in defining and annotating toxicity, balancing detoxification with generation quality, and extending the methods to diverse languages and contexts.

    Future work could explore more advanced detoxification methods, such as reinforcement learning, adversarial training, or model distillation. Developing better evaluation metrics that capture both the toxicity and coherence of generated text is also an important direction. Finally, studying the social and ethical implications of detoxification, such as the potential for censorship or bias, is crucial for responsible AI development.

    As language models become more powerful and widely deployed, ensuring their safety and robustness is a key challenge. The RealToxicityPrompts paper provides a valuable framework for studying this challenge and advancing the field of controllable language generation. With further research and refinement, we can develop LLMs that are both capable and ethical, generating useful and harmless content for a wide range of applications.

  • Global-survey-The-state-of-AI-in-2021 (1).pdf

    – Business adoption of AI is growing.
    – Companies using more sophisticated tools and practices are reaping benefits.

    – Companies using more sophisticated AI tools and practices have higher bottom-line benefits.
    – The most popular AI use cases span various functional activities.
    – Organizations following both core and advanced best practices see higher returns from AI.
    – AI can lead to revenue increase and cost decrease in organizations.
    – Cybersecurity is seen as a relevant AI risk, especially in developed economies.

    – Companies using more sophisticated AI tools and practices are reaping bottom-line benefits.
    – Popular AI use cases include logistics-network optimization, sales forecasting, and product enhancements.
    – Respondents report higher levels of cost decreases from AI adoption during the pandemic.
    – AI high performers prioritize training data, model documentation, and monitoring for bias.
    – Regular retraining and human-in-the-loop verification are important in model deployment.

    – Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
    – The most popular AI use cases span various functional activities.
    – Organizations with AI high performers engage in advanced best practices.
    – AI high performers prioritize training, testing, and monitoring of models.
    – Model bias and accuracy are important considerations for AI high performers.

    – Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
    – The most popular AI use cases span various functional activities.
    – AI high performers engage in practices such as training and testing data, measuring model bias and accuracy, and regularly monitoring for data drift.
    – Model users are taught how to monitor for issues and test for different outcomes based on protected characteristics.

  • GPT-3 and InstructGPT: technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry | AI and Ethics

    – The paper discusses the power, politics, and costs of Artificial Intelligence.
    – It references the book “Atlas of AI” by K. Crawford.
    – It also mentions the AI Now 2019 Report.

    – Potential shift of moral decision-making onto an unethical system.
    – Possibility of evading ethical responsibility.
    – Limitations of technological dystopianism and utopianism in understanding GPT-3.
    – Concerns about potential misuse applications of GPT-3.
    – Excitement within the NLP industry about the skilful tasks GPT-3 can perform.
    – Postulation that industry regulation is sufficient according to AI ethicists.

    – The paper discusses the power, politics, and costs of AI.
    – It highlights the limitations of technological dystopianism and utopianism.
    – It mentions concerns about GPT-3’s potential misuse and the need for regulation.

  • Global-survey-The-state-of-AI-in-2021.pdf

    – Business adoption of AI is growing.
    – Companies using more sophisticated tools and practices are reaping bottom-line benefits.

    – Companies using more sophisticated AI tools and practices have higher bottom-line benefits.
    – Popular AI use cases include product development, performance management, and user enablement.
    – Organizations following both core and advanced best practices see the highest returns from AI.
    – AI adoption can lead to revenue increase and cost decrease.
    – The paper highlights practices with the highest deltas between AI high performers and other respondents.

    – Companies using more sophisticated AI tools and practices are reaping bottom-line benefits.
    – Popular AI use cases include logistics-network optimization, sales and demand forecasting, and product-feature optimization.
    – Respondents report higher levels of cost decreases from AI adoption in the pandemic’s first year.

    – Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
    – The most popular AI use cases span various functional activities.
    – Organizations following both core and advanced best practices see higher returns from AI.

    – Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
    – The most popular AI use cases span various functional activities.
    – Organizations following core and advanced best practices see the highest returns from AI.
    – AI adoption leads to revenue increase and cost decrease for organizations.
    – The paper highlights practices with the highest deltas between AI high performers and other respondents.

  • On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

    – The paper discusses the development and deployment of large language models.
    – It explores the risks associated with these models and suggests mitigation strategies.
    – The authors recommend considering environmental and financial costs, curating datasets, and exploring research beyond large language models.

    The authors take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? They provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

    – The development of larger language models has been a trend in NLP.
    – The paper discusses the risks associated with big language models.
    – Recommendations include considering environmental and financial costs, curating datasets, and exploring research beyond large models.

    – The paper discusses the risks and possible mitigation strategies of large language models.

  • AI-video-editing-tools-What-editors-want-and-how-far-is-AI-from-delivering (1).pdf

    – Paper focuses on intelligent video editing tools
    – Surveys opinions of professional video editors

    – Identifies needs for automation in video editing workflow
    – Provides suggestions for further research in intelligent video editing tools

    – Surveyed opinions of video editors on ideal AI video editor
    – Findd personalization, voice interaction, and logging of videos

    – Intelligent video editing tools require expertise in video editing, human-computer interaction, and AI.
    – Areas such as logging, organization, aesthetic quality adjustment, and content suggestion need further exploration.

    – Average years of experience in video editing among participants is 9.75 years.
    – Most common video types are commercial, documentaries, presentation, sports, social media videos, and news.
    – Adobe Premier Pro and DaVinci Resolve are the most frequently mentioned editing programs.
    – Participants would like to interact with AI video editing tools via voice or graphical user interface.
    – Most participants would like to manipulate videos at the keyframes level.
    – Video editing tasks, aesthetic improvements, video pre-editing tasks, and suggestive tasks are the main areas participants want to automate.
    – Voice interaction is the most desired mode of interaction with AI video editors.
    – The paper compares and summarizes intelligent video editing tools in the literature.
    – The paper presents the survey results of human video editors’ opinions on ideal AI video editors.
    – The paper proposes potential AI techniques to meet the expectations of video editors.

  • IPOL_BRI(2021)662936_EN (1).pdf

    – The public sector aims to capture the benefits of using AI.
    – The EU AI strategy focuses on trust, excellence, and safeguarding rights.
    – Some AI applications in public services have not been developed responsibly.
    – The use of AI in public services has increased over the past two years.
    – Ensuring explainability of AI systems in public services is crucial.
    – There is growing public concern over the development and use of AI.
    – The public sector should lead in creating trustworthy AI.
    – Different definitions of AI in public services are in use.
    – Human responsibility in AI becomes marginalized as complexity increases.
    – Automation of decisions or services is an element of human-centeredness.
    – Public services include a combination of private and non-profit organizations.
    – Pre-procurement can be used to assess the feasibility of AI projects.
    – Multilingual datasets are used to train local AI models.

    – Access to data for training and testing is a barrier.
    – Complex data regulation landscape is a barrier.
    – Sharing of best practices is a barrier.
    – Alignment of AI strength and digital government goals is a barrier.
    – AI can bring efficiency gains and process optimization.
    – AI can reduce human error and fraud.
    – AI adoption depends on the level of digitization in society.

    – AI use in public sector is not lagging behind other sectors.
    – Benefits of AI in public sector: efficiency gains, less error/fraud, digitization.
    – Recommendations: promote Human Rights Impact Assessment, simplify regulatory landscape.
    – AI can improve digital services, predictive services, inclusiveness, and accessibility.
    – AI use in direct service delivery needs more evidence.

    – The uptake of AI in the EU public sector is not lagging behind other sectors.
    – Responsible development of AI in public services is crucial.
    – Some AI applications in public services pose unacceptable risks and are forbidden.
    – Public investments can accelerate the societal uptake of responsible AI.
    – Open Data can be used for AI to improve public services.
    – Ensuring explainability and trustworthiness of AI systems is challenging.
    – The public sector should lead in creating trustworthy AI.
    – Different definitions of AI in public services exist.
    – Automation of decisions or services in public services varies.
    – Access to data, complex regulations, and sharing best practices are barriers to uptake.

    – The paper discusses the benefits and challenges of using AI in public services.
    – It emphasizes the importance of responsible and human-centered AI development.
    – The public sector aims to lead in creating trustworthy AI.
    – The uptake of AI in the EU public sector is not lagging behind other sectors.
    – Some AI applications in public services, such as those manipulating human behavior, are considered unacceptable and forbidden.
    – Access to data, complex data regulation, and sharing of best practices are barriers to AI uptake in the public sector.
    – AI in public services focuses on law enforcement, surveillance, and process optimization.
    – The potential for errors and harms increases with the increased use of AI.
    – The paper concludes with recommendations for accelerating the uptake of responsible AI in public services.

    – Public sector should educate the public about AI through free education.
    – Services of general economic interest are subject to European rules.
    – Experimentation and regulatory sandboxing are important for developing trustworthy AI.
    – Multidisciplinary approach needed for AI development in the public sector.

  • AI-video-editing-tools-What-editors-want-and-how-far-is-AI-from-delivering.pdf

    – Video editing is time-consuming and tedious.
    – Artificial Intelligence (AI) is used to automate and streamline video editing.
    – This paper explores the field of intelligent video editing tools.
    – It surveys professional video editors to understand their opinions and needs.
    – The paper summarizes the current state of AI research in video editing.

    – Identifies needs for automation in video editing workflow
    – Highlights areas for further exploration in video editing tools
    – Calls for greater involvement of the machine learning community
    – Aims to contribute towards advancing the goal of an ideal AI editor

    – Survey conducted to explore opinions of video editors on AI video editing tools
    – Personalization and adaptation to user preferences is important
    – Voice interaction is desired by participants
    – Logging of videos can be done through audio annotations during filming

    – Intelligent video editing tools require expertise in video editing, human-computer interaction, and AI.
    – Areas such as logging, organization, aesthetic quality adjustment, and content suggestion need further exploration.
    – Involvement of the machine learning community can help achieve the ideal AI editor.
    – Voice interaction is the most desired mode of interaction by participants.
    – Five themes identified for the ideal AI video editing tool.

    – Average years of experience in video editing among participants is 9.75 years.
    – Most common video types are commercial, documentaries, presentation, sports, social media videos, and news.
    – Adobe Premier Pro and DaVinci Resolve are the most frequently mentioned editing programs.
    – Participants would like to interact with AI video editing tools via voice or graphical user interface.
    – Most participants would like to manipulate videos at the keyframes level.
    – Video editing tasks, aesthetic improvements, video pre-editing tasks, and suggestive tasks are the main areas participants want to automate.
    – Voice interaction is the most desired mode of interaction with AI video editors.
    – The paper compares and summarizes intelligent video editing tools in the literature.
    – The paper presents the survey results of human video editors’ opinions on ideal AI video editors.
    – The paper proposes potential AI techniques to meet the expectations of video editors.

  • 2304.02182v2.pdf

    – ChatGPT is a powerful pre-trained language model.
    – Naive prompts for ChatGPT have performance gaps.

    – ChatGPT can achieve better translation results than commercial systems.
    – Properly designed prompts can enhance ChatGPT’s translation performance.

    – Proposed translation prompts enhance ChatGPT’s translation performance.
    – ChatGPT achieves superior performance compared to commercial systems.

    – ChatGPT achieves better translation results than commercial systems.
    – Properly designed prompts can unleash ChatGPT’s translation power.

    – ChatGPT achieves superior performance compared to commercial translation systems.
    – Proposed translation prompts significantly enhance ChatGPT’s performance in translation.
    – Fewshot prompts consistently show improvement across different translation directions.

  • 2302.11382.pdf

    – Paper focuses on enhancing application of LLMs
    – Introduces prompt patterns for effective prompt engineering

    – The paper discusses the use of large language models (LLMs) like ChatGPT.
    – Prompt engineering techniques are used to converse effectively with LLMs.
    – Prompt patterns are reusable solutions to common problems in LLM conversations.
    – The paper provides a catalog of prompt patterns to improve LLM outputs.
    – Prompt patterns can be combined to enhance their effectiveness.

    – Prompt patterns enrich capabilities of conversational LLMs.
    – Prompt patterns can be combined to create larger and more complex capabilities.

    – Catalog of prompt engineering techniques for LLMs
    – Patterns to improve LLM conversation outputs

    – Prompt patterns enrich capabilities of conversational LLMs.
    – More work needed to refine and expand prompt patterns.

    – Provides a framework for documenting prompt engineering patterns.
    – Presents a catalog of prompt patterns to improve LLM outputs.
    – Explains how prompts can be built from multiple patterns.

  • 2302.06590.pdf

    – AI applications increase human productivity in various fields.
    – Little research on productivity impacts of AI-powered tools in professional contexts.

    – AI pair programmers can significantly increase developer productivity.
    – AI tools can help individuals transition into software development careers.

    – The treatment group with access to the AI pair programmer completed the task 55.8% faster than the control group.
    – AI pair programmers show promise in helping people transition into software development careers.

    – AI pair programmers can help software developers complete tasks faster.
    – The treated group completed the task 55.8% faster than the control group.
    – AI pair programmers show promise in supporting careers in software development.

  • 2302.06590 (1).pdf

    – AI applications increase human productivity in various fields.
    – Little research on productivity impacts of AI-powered tools in professional contexts.

    – AI pair programmers can significantly increase developer productivity.
    – AI tools can help individuals transition into software development careers.

    – The treatment group with access to the AI pair programmer completed the task 55.8% faster than the control group.
    – AI pair programmers show promise in helping people transition into software development careers.

    – AI tools can help software developers work faster and more efficiently.
    – In a study, developers using an AI pair programmer completed a task 55.8% faster.
    – The AI tool showed promise in helping people start careers in software development.
    – Developers with less experience and those who code more hours benefited the most.

  • 2111.04455 (1).pdf

    – AI-based language learning tools have impacted Second Language Acquisition.
    – The review focuses on AI tools developed between 2017 and 2020.

    – Teachers can gain knowledge and experience with AI tools.
    – Teachers can implement student-centered technology use.

    – AI-based language learning tools developed between 2017-2020
    – Tools utilized machine learning and natural language processing

    – AI-based language learning tools show positive effects on language abilities and knowledge.
    – Teachers can experiment with new technologies to gain knowledge and experience for student-centered technology use.

    – AI tools have been developed for various target language skill areas.
    – Tools include robots, mobile applications, and virtual assistants.
    – AI technologies used include NN, ML, and NLP.
    – Tools have been developed for speaking, listening, writing, pronunciation, grammar, vocabulary, and reading.
    – Tools have been developed for personalized voice models, grammar, and vocabulary.

  • 2302.11382 (1).pdf

    – Paper focuses on enhancing application of LLMs.
    – Introduces prompt patterns for effective prompt engineering.

    – The paper discusses the use of large language models (LLMs), such as ChatGPT.
    – LLMs are used to automate software development tasks.
    – Prompt engineering techniques are applied to converse effectively with LLMs.
    – The paper presents a catalog of prompt patterns to improve LLM outputs.
    – Prompt patterns provide reusable solutions to common problems in LLM conversations.

    – Prompt patterns enrich capabilities of conversational LLMs.
    – Prompt patterns can be combined to create larger and more complex capabilities.

    – Catalog of prompt engineering techniques for LLMs
    – Patterns to improve LLM conversation outputs and interactions

    – Prompt patterns enrich capabilities of conversational LLMs.
    – More work needed to refine and expand prompt patterns.

    – Framework for documenting prompt patterns for different domains.
    – Catalog of prompt patterns to improve outputs of LLM conversations.
    – Explanation of building prompts from multiple patterns.

  • 2111.04455.pdf

    – AI-based language learning tools have impacted Second Language Acquisition.
    – The review focuses on AI tools developed between 2017 and 2020.

    – Large-scale deep learning models including BERT and GPT-3 were mentioned.
    – There are plans to apply recently developed AI tools, including GPT-3, to language learning.

    – Teachers can gain knowledge and experience with AI tools.
    – Teachers can implement student-centered technology use.

    – AI-based language learning tools developed between 2017-2020
    – Tools utilized machine learning and natural language processing

    – AI-based language learning tools show positive effects on language abilities and knowledge.
    – Teachers can experiment with new technologies to gain knowledge and experience for student-centered technology use.

    – AI tools have been developed for various target language skill areas.
    – Tools include robots, mobile applications, and virtual assistants.
    – AI technology used includes NLP and ML.
    – Learners demonstrated gains in language abilities and knowledge.

    – AI tools help people learn languages more effectively and easily.
    – These tools can identify errors, provide feedback, and assess language abilities.
    – Learners who use these tools show improvement in their language skills.
    – Teachers can use AI tools to personalize language learning for students.
    – AI tools can be used for specific language skills like writing.

  • 2023-02-prompt-chatgpt-midjourney-ai-generators (1).pdf- “Mastering the Art of Prompt Writing for AI Generators” – “experienceing the Potential of Generative AI with Effective Prompts” – “The Power of Words: Enhancing AI Outputs through Prompt Engineering” – “From Vision to Reality: Perfecting Prompt Writing for AI Generators” – “Prompt Engineering: The Future Career Path in the Age of AI”

    – Generative AI models like ChatGPT and Midjourney are gaining popularity.
    – Prompt engineering is essential for experienceing their capabilities.

    – ChatGPT is a generative AI model mentioned in the text.

    – Prompt engineering is essential for experienceing generative AI’s capabilities.
    – Trial and error is key in perfecting prompt writing.

    – Generative AI is gaining popularity with ChatGPT and art generators like Midjourney.
    – Prompt engineering is crucial for experienceing AI’s capabilities.

    – Prompt engineering is essential for experienceing generative AI’s capabilities.
    – Trial and error and exploring style libraries are important.

    – The paper discusses how to perfect prompt writing for AI generators.
    – It mentions the potential of ChatGPT and art generators like DALL-E 2, Stable Diffusion, and Midjourney.
    – It provides examples of using specific commands and prompts to create desired images.
    – It shows that elaborate prompts are not necessarily better for image generation.
    – It demonstrates the use of style keywords to create a variety of interesting images.

    – Generative AI can create images based on simple prompts and style keywords.
    – You can use specific commands to control certain features of the generated images.
    – Generative AI models have limitations and may not always make sense.
    – Prompt engineering is important for getting the desired results from AI models.

  • 2210.09150 (1).pdf- “experienceing the Power of Prompts: Enhancing GPT-3’s Reliability” – “Improving GPT-3’s Performance: Insights from Prompting Strategies” – “Enhancing GPT-3’s Reliability: Simple and Effective Prompting Techniques” – “Maximizing GPT-3’s Potential: Strategies for Reliable Language Generation” – “From Bias to Factuality: Enhancing GPT-3’s Reliability with Prompts”

    – Large language models (LLMs) are dominant in NLP.
    – GPT-3 is a popular and flexible LLM.

    – GPT-3 is more robust than supervised models.
    – GPT-3 can update its knowledge with counterfactual passages.
    – GPT-3 has better calibration than supervised DPR-BERT model.
    – Increasing the number of demos in the prompt improves accuracy.
    – GPT-3’s confidence scores are more discriminative.
    – GPT-3 can improve factual QA via retrieval-augmented prompts.

    – Provides practical recommendations for users of GPT-3.
    – Inspires future work on examining more facets of reliability and applying prompting methods to real-world applications.

    – GPT-3’s reliability can be improved through effective prompts.
    – Reliability is enhanced in terms of generalizability, social biases, calibration, and factuality.

    – Effective prompting strategies improve GPT-3’s reliability.
    – GPT-3 outperforms supervised models on multiple facets.

    – GPT-3 is better calibrated than supervised DPR-BERT.
    – Increasing the number of examples in the prompt improves accuracy.
    – GPT-3 has similar calibration regardless of the source of examples.
    – GPT-3’s confidence scores are more discriminative.
    – Selective prediction based on GPT-3 confidence scores is effective.

    – GPT-3 is a language model that can understand and generate text.
    – It can be unreliable because it may not always give correct answers.
    – Researchers have found ways to make GPT-3 more reliable.
    – They use different types of example questions to train GPT-3.
    – GPT-3 can learn from new information and update its knowledge.
    – Users need to be careful and not blindly trust all of GPT-3’s predictions.
    – GPT-3 can give confidence scores to show how likely its answers are correct.

  • 2023-02-prompt-chatgpt-midjourney-ai-generators.pdf

    – Generative AI is gaining popularity with ChatGPT and art generators like DALL-E 2, Stable Diffusion, and Midjourney.
    – People are trying to figure out how to achieve their desired outputs with these AI models.

    – Prompt engineering is essential for experienceing generative AI’s capabilities.
    – Trial and error is key in perfecting prompt writing.

    – Generative AI is gaining popularity with ChatGPT and art generators like Midjourney.
    – Prompt engineering is crucial for experienceing AI’s capabilities.

    – Prompt engineering is essential for experienceing generative AI’s capabilities.
    – Trial and error and exploring style libraries are important.

    – The paper discusses how to perfect prompt writing for AI generators.
    – It mentions the potential of ChatGPT and art generators like DALL-E 2, Stable Diffusion, and Midjourney.
    – It provides examples of using specific commands and prompts to create desired images.
    – It shows that elaborate prompts are not necessarily better for image generation.
    – It demonstrates the use of style keywords to create a variety of interesting images.