This is an AI agent tool that uses 3 agent personas to analyze arxiv papers from 3 different aspects which are integrated in an AI article summary. I have not seen this innovation before in AI article summarization tools. For some reason the tool does not seem to work on summarizing very recent arxiv articles. The tool is discussed in the post below:
https://news.ycombinator.com/item?id=40653990
The tool itself is accessed at:
https://www.stack-ai.com/
A Mire jó nagy nyelvi modell mai jelöltje https://kozlonyertelmezo.ai/ #llm #summarization
🤖 Resource-Efficient & Effective Code Summarization
(funny, it's harder to make an AI tell what code does than it is to make one write code...)
https://arxiv.org/abs/2502.03617
#ai #llm #ml #summarization #coding #programming #reverseengineering
📚 **AI-Powered Study Guide Summarization!** 🤖✨
Struggling with **long study materials**? Learn how **Prompt Engineering** can help you craft AI queries that generate **concise, structured, and effective summaries**! 🧠🔍
📖 https://www.promptengineering.ninja/p/prompt-engineering-for-summarizing
#AI #PromptEngineering #StudySmart #EdTech #MachineLearning #ArtificialIntelligence #Summarization #AIinEducation
Video: #PromptFlow tools are an excellent way to provide value to your Prompt Flow with minimal configuration. How can you add to the current tools? In these 2 videos we will add #CosmosDB vector Store, #Summarization, #Translator, #PII and compression tools.
https://youtu.be/nomL8yHW-7U
https://youtu.be/mfS37zACihs
@thetrainingboss @AzureAIFoundry
→ We’re Doing What Searchbots Can’t
https://thewalrus.ca/were-doing-what-searchbots-cant/
“[Summarization tools incorporated into the search engines are] a boon for people seeking quick answers, but a bane for publishers. Disincentivizing curious users from clicking through to a news site for additional information—a trend called zero-click search—sends less traffic to media outlets that invest in the costly #reporting that #AI machines are scraping, strip-mining, and synthesizing.”
eShop infused with AI – a comprehensive intelligent app sample
https://devblogs.microsoft.com/dotnet/e-shop-infused-with-ai-comprehensive-intelligent-dotnet-app-sample/
#microsoft #NET #NET_Aspire #NET_Core #AI #ASP_NET_Core #Blazor #C #Cloud_Native #chatbot #classification #e2e_testing #evaluation_tool #intelligent_apps #semantic_search #sentiment_scoring #summarization #test_data_generation
As AI continues to gift society with condensed information, the question arises: are we becoming dumber or simply more efficient? It's crucial that this technology sparks curiosity rather than breeding complacency. #AI #Society #Summarization
#Summarization: "Australian Government Trial Finds #AI is Much Worse Than Humans at Summarizing" & More AI News Headlines https://ow.ly/fNho50TfUAu
Обзор приложения NotebookLM
Приложение под названием NotebookLM ( https://notebooklm.google.com/ ) было выпущено компанией Google около года назад, и на Хабре было по этому поводу два кратких анонса в прошлом году ( раз , два ). На мой взгляд, оно заслуживает обзора чуть более подробного чем эти краткие сообщения, так что попробую восполнить этот пробел. NotebookLM - это инструмент на основе ИИ, который позволяет относительно быстро, удобно и без лишних телодвижений получить краткий разносторонний обзор (саммари) объемных документов (книг, статей), а также интерактивно взаимодействовать с ними (задавать вопросы, касающиеся их содержания). В моем понимании он представляет собой надстройку над "обычным ИИ-чатом", которому в контекст загружен интересующий пользователя документ. Эта надстройка включает в себя: 1. Набор из нескольких преднастроенных стандартизованных промптов, доступных в один клик и ориентированных на работу с объемными текстами ("Составь мне оглавление", "Составь мне FAQ на основе этого текста", и т.п.) 2. Интерфейсное решение ("карточки-плитки на рабочем столе"), которое по замыслу разработчиков, видимо, должно быть более удобным чем "обычный (линейный) чат" 3. Интерфейс чата, который при взаимодействии с текстом в формате "вопрос-ответ" отображает не только ответы на задаваемые вопросы, но и фрагменты соответствующего исходного текста, а также ссылки на конкретные параграфы полного текста-источника. Посмотрим как это работает
📚🤖 Dive into the world of AI-powered summarization! 🧠 The difference between truly summarizing and just shortening text. Understand how LLMs craft concise yet meaningful summaries, and why it's more than just cutting words. ChatGPT and Claude have there say: 🔍💡
"Understanding LLM Summarization
Let’s delve into the difference between summarizing and simply shortening text, as well as addressing those 'gotcha' critiques you mentioned.
Summarizing vs. Shortening
Summarizing involves capturing the main ideas and key points of a text in a concise manner. It's about distilling the essence of the information while ensuring the core message is preserved. This process requires understanding the text deeply and conveying the main themes and conclusions.
Shortening, on the other hand, is merely reducing the length of the text without necessarily maintaining the original meaning or context. This can lead to loss of important details and nuances, making the shortened text less informative or coherent.
Example for Clarification
Imagine a paragraph discussing the importance of holistic health:
Original Text:
Holistic health is an approach to wellness that considers the whole person, emphasizing physical, mental, emotional, and spiritual well-being. Unlike traditional medicine, which often focuses on treating symptoms, holistic health seeks to identify and address the root causes of health issues. By integrating various therapies such as nutrition, exercise, stress management, and alternative medicine, holistic health aims to promote overall balance and harmony in one's life.
Summarized:
Holistic health focuses on overall well-being, addressing physical, mental, emotional, and spiritual aspects. It aims to find and treat the root causes of health issues through integrated therapies like nutrition, exercise, and stress management.
Shortened:
Holistic health considers physical, mental, and spiritual well-being, unlike traditional medicine.
In the summarized version, the main points and intent of the original text are preserved. The shortened version, however, loses significant details and nuances.
Addressing Critiques
When critics argue that summarization is just shortening, they might overlook the nuanced approach of true summarization, which retains the essence and intent of the original content. To ensure summaries are accurate and meaningful, it's essential to:
- Understand the Content Deeply: Read thoroughly and identify the key points.
- Capture the Main Ideas: Focus on what is most important in the text.
- Preserve the Context: Ensure the summary reflects the original intent and message.
- Be Concise: Convey the core message without unnecessary details.
By maintaining this approach, summaries become effective tools for conveying information clearly and succinctly without losing meaning.
Why the Perception Exists
1. Complexity of Language: Human language is intricate, and summarizing involves not just shortening but understanding and conveying the core message, which can be challenging.
2. Output Limitations: Sometimes, the output might appear as just shortened text rather than a true summary, leading to the belief that LLMs don't fully grasp the content.
3. Variability in Quality: The quality of summaries can vary depending on the complexity of the input text and the context provided.
How LLMs Create Summaries
1. Contextual Understanding: LLMs analyze the context and content of the text to identify key points and main ideas.
2. Pattern Recognition: They recognize patterns and themes within the text, which helps in distilling the essence.
3. Conciseness: LLMs aim to convey the main message concisely while preserving essential details and context.
Demonstration with an Example
Let’s take a passage and show how an LLM can summarize it accurately.
Original Text:
Philosophy and psychology are two distinct fields that often intersect. Philosophy is the study of fundamental questions about existence, knowledge, and ethics, while psychology focuses on understanding the human mind and behavior. Despite their differences, both disciplines offer valuable insights into human nature and can complement each other. For example, philosophical discussions about morality can inform psychological studies on ethical behavior, and psychological findings about cognition can enrich philosophical theories of knowledge.
Summarized by an LLM:
Philosophy and psychology intersect in studying human nature. Philosophy addresses fundamental questions about existence and ethics, while psychology examines the mind and behavior. Both fields complement each other, with philosophical discussions on morality informing psychological studies on ethics and psychological findings enriching philosophical theories.
Explanation
- Key Points: The summary captures the main ideas: the intersection of philosophy and psychology, their focus areas, and how they complement each other.
- Context and Essence: The essence of how the two fields interact and enrich each other is preserved.
- Conciseness: The summary is concise, avoiding unnecessary details while maintaining the core message.
Continuous Improvement
LLMs are continually improving in their ability to summarize effectively. Feedback and advanced training help enhance the accuracy and quality of summaries."
I wonder if these models really are to good at sounding sensible now? Has anyone got the time and energy to go through something like this and fact/sanity chest it? I got it from ChatGPT and asked Claue to fact check it - but honestly, who knows?
Как анализировать тысячи отзывов с ChatGPT? Частые ошибки и пример на реальных данных
В этой статье я расскажу про свой опыт решения рабочей задачи — анализ отзывов о компании от пользователей. Мы разберем возможные ошибки и посмотрим на пример кода и реальных данных. Гайд будет полезен всем, у кого нет большого опыта в анализе данных или работе с LLM через API.
https://habr.com/ru/articles/821287/
#llm #gpt #chatgpt #python #clustering #kmeans #tsne #visualization #summarization #data_analysis
Автоматизируем поиск ценной информации в групповых чатах Telegram с помощью LLM
Устали мониторить бесконечные групповые чаты в Telegram в поисках важной информации? Решение есть! Пишем компактное приложение на Python, которое будет делать это за нас с использованием LLM.
https://habr.com/ru/articles/804111/
#telegram #chatgpt #llm #summarization #автоматизация #боты #gpt #python
As the #GEM team already mentioned, we have endorsed the #data2text and #summarization shared tasks taking place this year: https://gem-benchmark.com/shared_task
For data-to-text, there are two different datasets and you can choose to work with factual, counterfactual, or fictional versions of the datasets.
For summarization, you can work on Swahili, cross-lingual summarizaion, or summarizing English book chapters.
Interesting challenges with a deadline of 5 April with human evaluations starting on the 6th
New efficient eval results
1. A few examples are enough for Human preference to be clear, automatic metrics also don't need too many
2. Context may change which model is preferred
https://arxiv.org/abs/2402.18756
#evaluation #nlp #nlproc #ML #summarization #efival
Предсказать ошибку. Как методы оценки неопределенности помогают повышать качество seq2seq-моделей
Всем привет! Меня зовут Артём Важенцев , я аспирант в Сколтехе и младший научный сотрудник AIRI. Наша группа занимается исследованием и разработкой новых методов оценивания неопределенности для языковых моделей. Этим летом мы опубликовали две статьи на ACL 2023 . Про одну из них я уже рассказывал в одном из предыдущих текстов — там мы описали новый гибридный метод оценивания неопределенности для задачи выборочной классификации текстов. Другая же статья про то, как мы адаптировали современные методы оценивания неопределенности на основе скрытого представления модели для задачи генерации текста, а так же показали их высокое качество и скорость работы для задачи обнаружения примеров вне обучающего распределения. Ниже я хотел бы подробнее рассказать об используемых методах и результатах, которые мы получили.
https://habr.com/ru/companies/airi/articles/787340/
#uncertainty_estimation #natural_language_processing #machine_translation #question_answering #summarization #seq2seq
If you're looking for a hype-free, simple, practical project to get started with Generative AI, check out our latest podcast and video. We are finding a lot of real applications internally and for clients when it comes to understanding and adopting #GenAI tools and services like #Bedrock. This project comes with source code and built-in tooling for cost monitoring 😌
https://awsbites.com/103-building-genai-features-with-bedrock
#LLM #Anthropic #Claude2 #summarization
"Automatic Aspect Extraction from Scientific Texts"
Therefore, the aim of our research is to create a tool for automatic aspect extraction from Russian-language scientific texts of any domain. In this paper, we present a cross-domain dataset of scientific texts in Russian, annotated with such aspects as Task, Contribution, Method, and Conclusion, as well as a baseline algorithm for aspect extraction, based on the multilingual BERT model ...
Yesterday at #TPDL2023 David Pride presented “CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering”
Rather than #ZeroShot question/answering, Pride’s team combines the #CORE #OpenAccess dataset with #ElasticSearch to create #FewShot prompts that leverage the strength of combining #search results with the #LLM’s (#GPT) #summarization abilities to produce an answer to a user’s question including citations.
What about that metadata that is present? Grusky et al. (https://doi.org/10.18653/v1/N18-1065 ) realized that, because page authors create that metadata, it can serve as ground truth to evaluate #Automatic #Summarization.
We analyzed pages from #WebArchiving and saw how this metadata evolved. By 2010 we saw a metadata explosion with the use of #Twitter Cards, Open Graph Protocol, #Facebook Tracking, and more. Things like Twitter cards created a metadata renaissance for HTML.
