Tag: academics

  • 2210.09150.pdf- “experienceing the Power of Large Language Models: A Guide” – “Improving Reliability in Language Models: Strategies and Techniques” – “Harnessing the Potential of GPT-3: Tips and Tricks” – “Exploring the Four Facets of Reliability in GPT-3” – “Prompts that Enhance GPT-3’s Performance: A Deep Dive” – “Demystifying the Magic of GPT-3: Behind the Scenes” – “From Prompting to Generalizability: Unleashing GPT-3’s Potential” – “Addressing Social Biases in GPT-3: A Promising Approach” – “Calibration and Factuality: Key Aspects of Reliable Language Models” – “Maximizing the Reliability of GPT-3: Best Practices and Insights”

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

    – GPT-3 is a large language model (LLM) that is popular and easy to use.
    – GPT-3 can be prompted with natural language text to shape predictions.
    – GPT-3’s reliability can be improved through effective prompts.
    – GPT-3 outperforms smaller-scale supervised models in terms of reliability.

    – 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.

    – The paper explores how to improve the reliability of GPT-3.
    – It focuses on four facets of reliability: 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.

    – Large language models (LLMs) are powerful tools for understanding and generating text.
    – GPT-3 is a popular LLM that is easy to use.
    – GPT-3 can be made more reliable by using specific prompts.
    – Reliability includes factors like generalizability, social biases, calibration, and factuality.
    – Prompting strategies can help practitioners use GPT-3 more reliably.

  • 2303.07839.pdf

    – Large-scale language models (LLMs) automate software engineering tasks.
    – Prompt patterns improve code quality and requirements elicitation.

    – Prompt patterns can help reduce errors in software engineering tasks.
    – LLMs have the potential to automate common software engineering tasks.

    – Paper presents prompt design techniques for software engineering.
    – Provides catalog of prompt patterns for software engineering.

    – LLMs have immense potential for automating software engineering tasks.
    – Prompt patterns can help mitigate issues with LLM output.

  • 2303.07839 (1).pdf

    – Large-scale language models (LLMs) automate software engineering tasks.
    – Prompt patterns improve code quality and requirements elicitation.

    – The paper mentions the use of large language models (LLMs) like ChatGPT.
    – LLMs have capabilities to generate synthetic data and simulate APIs.
    – Prompt patterns are used to guide LLMs in performing software engineering tasks.

    – LLMs have the potential to automate software engineering tasks.
    – Prompt patterns can help mitigate issues with LLM output.

    – Paper presents prompt design techniques for software engineering.
    – Provides catalog of prompt patterns for software engineering.

    – LLMs have immense potential for automating software engineering tasks.
    – Prompt patterns can help mitigate issues with LLM output.

    – Catalog of patterns for software engineering to solve common problems.
    – Exploration of prompt patterns for requirements elicitation, code quality, etc.

  • 2305.08360 (1).pdf

    – Evaluating ChatGPT for code generation tasks
    – Proposing prompt design and optimization methods

    – Improved prompts for guiding ChatGPT in code generation.
    – Evaluation of ChatGPT’s performance on CodeXGlue dataset.

    – Evaluating ChatGPT on CodeXGlue dataset for code generation tasks.
    – Proposing prompt design and optimization methods for better code generation.

    – Prompts improved ChatGPT’s code generation performance substantially.
    – Factors influencing prompt design for code generation tasks were analyzed.

    – Experimental settings and results for four research questions were presented.
    – Limited testing was conducted to improve prompt design for ChatGPT.
    – Human evaluation of 100 randomly selected samples provided insights for further studies.

  • 2304.02182v2 (1).pdf- “Unleashing the Translation Power of ChatGPT: Designing Effective Prompts” – “Enhancing Translation Performance with Context Domain Information in ChatGPT” – “Improving Translation Quality with Few-shot Prompts in ChatGPT” – “The Impact of Correct Information in Prompts on ChatGPT’s Translation” – “Exploring the Effectiveness of Translation Prompts in ChatGPT” – “In-context Learning for Language Models: Advantages and Applications” – “Prefix-tuning and Few-shot Learning: Boosting Language Models for Translation”

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

    – ChatGPT is a powerful pre-trained language model developed by OpenAI.
    – ChatGPT is built upon GPT-3.5 and optimized with Reinforcement Learning from Human Feedback (RLHF).
    – ChatGPT has surprising abilities in natural language understanding and generation.
    – ChatGPT can perform human-like tasks such as writing poems and fixing coding bugs.
    – ChatGPT exhibits a performance gap compared to other commercial translation systems.

    – 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 systems in translation.
    – Incorporating POS tags boosts ChatGPT’s performance in many translation directions.
    – However, there is a performance drop in some translation directions.

    – ChatGPT is a language model that can understand and generate sentences.
    – It can also be used for machine translation, which means translating sentences from one language to another.
    – The researchers in this paper found that using specific prompts can make ChatGPT better at translation.
    – They tested different prompts and found that they improved ChatGPT’s translation performance.
    – They also tested prompts that only had a few examples, and those also helped improve translation.
    – Overall, using the right prompts can make ChatGPT better at translating sentences.

  • 2305.08360.pdf

    – Evaluating ChatGPT for code generation tasks
    – Proposing prompt design and optimization methods

    – ChatGPT is a language model used for generating human-like responses.
    – ChatGPT was evaluated for code generation tasks using the CodeXGlue dataset.
    – The prompt design was found to significantly improve the generation performance.
    – The performance of the best prompts was compared with state-of-the-art finetuned LLMs.
    – CodeBLEU was used as the overall evaluation metric for code generation.

    – Improved prompts for guiding ChatGPT in code generation.
    – Evaluation of ChatGPT’s performance on CodeXGlue dataset.

    – Evaluating ChatGPT on CodeXGlue dataset for code generation tasks.
    – Proposing prompt design and optimization methods for better code generation.

    – Prompts improved ChatGPT’s code generation performance substantially.
    – Factors influencing prompt design for code generation tasks were analyzed.

    – Experimental settings and results for four research questions.
    – Comparison with benchmark models and related works.

  • 2022.findings-acl.50.pdf

    – Paper focuses on improving GPT3’s response to instructions
    – Provides reframing principles to address GPT3’s failures

    – The paper mentions GPT3’s poor performance in following task instructions.

    – Guidelines for reframing instructional prompts for LMs
    – Improved performance and sample complexity with reframing

    – GPT3’s response to instructions improves with short, concrete prompts.
    – Reframing techniques can resolve GPT3’s failures with prompting.

    – Reframing improves upon few-shot and zero-shot baselines.
    – Highest gains in Answer Generation, Classification, and Verification categories.

    – Reframing improves upon few-shot and zero-shot baselines.
    – Reframing outperforms the original raw instruction baseline.

    – The paper discusses challenges in programming language models for complex tasks.
    – It mentions errors in understanding instructions and generating incorrect outputs.
    – Different reframing techniques are used to resolve these errors.
    – Evaluation tasks like WinoGrande and QASC are used in the study.

  • A_Prompt_Pattern_Catalog_to_Enhance_Prompt_Engineering_with_ChatGPT (1).pdf

    – Paper focuses on prompt engineering for conversing with LLMs.
    – Describes a catalog of prompt engineering techniques.

    – Provides a framework for documenting prompt patterns
    – Enriches capabilities in conversational LLMs

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

    – Framework for documenting and applying prompt patterns
    – Prompt patterns significantly enrich capabilities of LLMs

    – Framework for documenting prompt engineering patterns
    – Catalog of patterns to improve LLM outputs

  • 2022.findings-acl.50 (1).pdf

    – Paper focuses on improving GPT3’s response to instructions
    – Provides reframing principles to address GPT3’s failures

    – The paper mentions GPT3’s poor performance in following task instructions.

    – Guidelines for reframing instructional prompts for LMs
    – Improved performance and sample complexity with reframing

    – GPT3’s response to instructions improves with short, concrete prompts.
    – Reframing techniques can resolve GPT3’s failures with prompting.

    – Reframing improves upon few-shot and zero-shot baselines.
    – Highest gains in Answer Generation, Classification, and Verification categories.

    – Reframing improves upon few-shot and zero-shot baselines.
    – Reframing outperforms the original raw instruction baseline.

    – The paper discusses challenges in programming language models for complex tasks.
    – It mentions errors in understanding instructions and generating incorrect outputs.
    – Different reframing techniques are used to resolve these errors.
    – Evaluation tasks like WinoGrande and QASC are used in the study.

  • A_Prompt_Pattern_Catalog_to_Enhance_Prompt_Engineering_with_ChatGPT.pdf

    – Paper focuses on prompt engineering for conversing with LLMs.
    – Describes a catalog of prompt engineering techniques.

    – The paper discusses prompt engineering techniques for ChatGPT.
    – Prompt patterns are used to customize outputs and interactions with LLMs.
    – The catalog provides reusable solutions to common problems in LLM conversations.
    – Prompt patterns can be combined to enhance the effectiveness of prompts.
    – The paper presents a framework for documenting and applying prompt patterns.

    – Provides a framework for documenting prompt patterns
    – Enriches capabilities in conversational LLMs

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

    – Framework for documenting and applying prompt patterns
    – Prompt patterns significantly enrich capabilities of LLMs

    – Framework for documenting prompt engineering patterns
    – Catalog of patterns to improve LLM outputs

  • org.pdf

    – Testosterone is a hormone that affects various bodily functions.
    – As we age, testosterone levels naturally decrease, impacting health.
    – Low testosterone levels can lead to fatigue, mood shifts, and diminished sexual desire.
    – Natural methods to increase testosterone include dietary changes, exercise, and supplements.
    – This shift towards natural methods is rooted in research and scientific backing.
    – Setting realistic expectations is important when boosting testosterone naturally.

    – Natural methods can increase testosterone levels and improve overall health.
    – Lifestyle modifications and dietary strategies are effective in boosting testosterone.
    – Vitamins and minerals like Vitamin D, B6, Zinc, and Magnesium promote testosterone growth.
    – Long-term commitment to natural ways leads to demonstrable boosts in testosterone levels.

    – Natural methods can be used to increase testosterone levels gradually.
    – Consistency and commitment are necessary for achieving healthy testosterone levels.
    – Misconceptions about quick-fixes and instant results need to be debunked.
    – Natural testosterone boosters can improve testosterone production.
    – Further research is needed to understand the impact of individual factors.
    – Long-term commitment to natural ways can lead to demonstrable boosts in testosterone levels and overall health improvement.

    – Maintaining high testosterone levels can improve energy, muscle mass, and weight management.
    – Targeting testosterone can lead to improved mood, cognitive ability, and emotional stability.
    – Strategies for natural testosterone enhancement include strength training, dietary changes, and better sleep hygiene.
    – Natural supplements like Vitamin D, zinc, and herbs can support testosterone growth.
    – Commitment to natural ways can boost testosterone levels and overall health improvement.
    – Research supports the positive influence of lifestyle modifications and natural supplements on testosterone production.

  • 375.pdf

    – Alkylation of the 17a position of testosterone allows oral administration.
    – Fluoxymesterone and Oxandrolone are orally active testosterone derivatives.
    – Hepatic adverse effects have been associated with 17a-alkylated androgens.
    – Androgens can cause masculinization, acne, facial hair growth, and more.
    – Testosterone should not be used by pregnant women.
    – Excess androgens can cause priapism, impotence, and gynecomastia.

    – Androgens can cause masculinization and acne in females.
    – Testosterone should not be used by pregnant women.
    – Excess androgens can cause priapism, impotence, and gynecomastia.
    – Alkylation of testosterone allows oral administration of the hormone.
    – Hepatic adverse effects have been associated with 17a-alkylated androgens.

    – Androgens can cause masculinization, acne, facial hair growth, and more.
    – Testosterone should not be used by pregnant women.
    – Excess androgens can cause priapism, impotence, and gynecomastia.
    – Alkylation of testosterone allows oral administration.
    – Fluoxymesterone and oxandrolone are orally active testosterone derivatives.
    – Hepatic adverse effects associated with 17a-alkylated androgens.

    – Androgens can cause masculinization, acne, facial hair growth, deepening of voice.
    – Excessive muscle development and male pattern baldness can occur.
    – Menstrual irregularities may occur in females.
    – Testosterone should not be used by pregnant women.
    – Excess androgens can cause priapism, impotence, decreased spermatogenesis, and gynecomastia.
    – Cosmetic changes may occur in females.
    – Androgens can stimulate growth of the prostate.
    – Fluoxymesterone and oxandrolone are orally active testosterone derivatives.
    – Hepatic adverse effects have been associated with 17a-alkylated androgens.
    – Alkylation of the 17a position of testosterone allows oral administration.
    – Fluoxymesterone has a longer half-life in the body than natural androgens.

  • azure-ai-services-openai.pdf

    – Prompt construction is important but challenging in GPT models.
    – Text prompts are used to interact with GPT models.
    – GPT models aim to generate the most likely next series of words.

    – Azure OpenAI supports Java & JavaScript SDK for chat completion.
    – Microsoft is committed to responsible AI use and mitigations.
    – Access to Azure OpenAI is currently limited, with specific criteria.
    – Microsoft has made investments to guard against abuse and harm.

  • whisper.pdf

    – Speech recognition systems have been improved through unsupervised pre-training techniques.
    – Pre-trained audio encoders lack a performant decoder, requiring finetuning.
    – Fine-tuning can lead to overfitting and limited generalization to other datasets.
    – Existing supervised datasets for speech recognition are limited in size.
    – Weakly supervised pre-training with larger datasets improves robustness and generalization.
    – This paper introduces Whisper2, a weakly supervised speech recognition approach.
    – Whisper2 scales weakly supervised pre-training to 680,000 hours of labeled audio data.
    – Models trained with Whisper2 transfer well to existing datasets without finetuning.
    – Whisper2 focuses on multilingual and multitask training, covering 96 languages.
    – The paper releases inference code and models for further research on robust speech recognition.

    – Speech processing models trained on large amounts of internet transcripts.
    – Models generalize well to standard benchmarks without finetuning.
    – Models approach human accuracy and robustness.
    – Models and inference code released for further research on speech processing.

    – Scaling weakly supervised pretraining improves robustness in speech recognition.
    – Large and diverse supervised datasets can enhance zero-shot transfer performance.
    – No need for self-supervision and self-training techniques.
    – Models approach human accuracy and robustness.
    – Models generalize well to standard benchmarks without finetuning.

    – Models trained on 680,000 hours of weakly supervised data generalize well to standard benchmarks.
    – Models achieve competitive results without the need for fine-tuning.
    – Models approach human accuracy and robustness in speech recognition.

  • stateofgpt.pdf

    – The paper discusses the process of “finetuning” GPT-2 on a small supervised dataset.
    – It explores how GPT-2 can be tricked into performing a specific task.
    – The paper mentions the relevance of the term “monopsony” in economics.
    – It provides examples related to potential monopsonies in the labor market.
    – The paper cites relevant research on the topic.

    – Monopsony means there is only one buyer in a market.
    – In labor market, monopsony employer has power over wages and conditions.
    – Monopsony can lead to lower wages and limited job opportunities.
    – Research found potential monopsonies in retail and fast food industries.
    – Workers in these industries face low wages and limited benefits.
    – Monopsony can result in dependence on employer for livelihood.
    – This can further suppress wages and decline working conditions.