Peter, Jolly Ikhine’s Post

#GlitchTokens are like weed in a crop, it can reduce the yield . Similarly a Glitch token can impact the performance of LLM Unlike words, which represent clear and discrete units of meaning in human language, tokens in LLMs can represent anything from whole words to fragments of words or even punctuation. This modular approach allows LLMs to process and generate language with remarkable efficiency and subtlety. But what about glitch tokens? Occasionally, the tokenization process can produce unexpected results—these are known as glitch tokens. These anomalies can occur due to the complex interplay of encoding and training data irregularities. For instance, a glitch token might represent a piece of text that doesn't conform to typical linguistic patterns due to encoding errors or unusual data inputs. Understanding these glitches is crucial for refining model outputs and enhancing the overall robustness of LLMs. A proper training of LLM reduces the changes of glitch, there are ways to find the glitch tokens . I will try to put more about the same in future. #AI #tokens #LLM

One of the most relevant risks for AI. #ModelColapse Project Analyzing Human Language Usage Shuts Down Because ‘Generative AI Has Polluted the Data’ https://lnkd.in/gTFM4a9a #responsibleai #aisafety #aigovernance #aialignment

Llama 3.1 Models: AI at Meta has released the Llama 3.1 collection of multilingual large language models (LLMs). These models come in various sizes, including 8B, 70B, and a groundbreaking 405B parameter variant. The Llama 3.1-405B is now one of the largest and most powerful open-source language models available, rivaling proprietary models from companies like OpenAI and Alphabet. Read more about what #MarkZuckerberg said in the AI manifesto, + more AI updates. All for free, choose what 65K+ readers chose- https://lnkd.in/d5UrNVtT #ai #artficialintelligence #googleai #googledeepmind #ainews #aitutorials #aiforbusiness #aiinnovation #aitech #agi #genai #googleupdates #aiupdates #ainewsletter #openai #chatgpt #chatgpt4 #metaai #facebook #meta

The creator of the open-source project WordFreq, which tracks how popular different words are online, announced that they’re shutting it down. They say that generative AI spam has polluted the internet to a level where the project isn’t useful anymore. https://lnkd.in/eUwMdw6r #ai #genai

Meet the new competitor: LLaMA 3 (Large Language Model Meta AI) is the latest iteration in Meta's series of large language models. It's designed to be more powerful, efficient, and versatile than its predecessors. Some key aspects likely include improved natural language understanding and generation capabilities, better performance on a range of tasks, and enhanced ability to handle complex queries and conversations. Meta's advancements in this area often focus on scaling up model size, fine-tuning techniques, and ensuring ethical use and transparency. Have you been following any specific developments or applications of LLaMA 3? AI is going crazy these days 😅 #AI #Llama3 #meta #openAi #openai

🚀 As the digital landscape evolves, so do we. Our advanced platforms harness the power of large language models (LLM) and AI-assisted review to streamline the identification of crucial information. Say hello to efficiency and goodbye to time-consuming searches! #DigitalInnovation #AI #AdvancedTechnology https://lnkd.in/gNEwq9zU

Automated Unit Test Improvement using Large Language Models at Meta This paper describes Meta’s TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGenLLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms. In an evaluation on Reels and Stories products for Instagram, 75% of TestGen-LLM’s test cases built correctly, 57% passed reliably, and 25% increased coverage. https://lnkd.in/eRKGuNzx #llm #ai #unittesting #largelanguagemodels #automatedtesting

Understanding 'Hallucinations' in Large Language Models Large language models (LLMs) are incredible, but they can generate text that's factually wrong, nonsensical, or unrelated to the input – this is called "hallucination." Why is this a problem? Inaccurate 'facts': A model may sound convincing but be completely incorrect. Source confusion: LLMs can conflate information, leading to errors. Ethical Dilemmas: Hallucinations fuel misinformation, especially in sensitive fields like healthcare or law. It's complex! Causes include training data issues, internal biases, or just the complexity of language. We can mitigate the risks (better prompts, fine-tuning), but can't eliminate them entirely. Key takeaway: LLMs are powerful but fallible. Being aware of hallucinations is critical for using these models responsibly. https://lnkd.in/g2y5cPQQ #LLM #naturallanguageprocessing #AI #machinelearning

Fake news using AI prevalent on Chinese language social media - AsAmNews: Fake news using AI prevalent on Chinese language social media  AsAmNews http://dlvr.it/T9sBSV #ai #artificialintelligence

🌐 The Power of Language Models 🧠 We can all agree on the incredible potential that Language Models (LMs) have unlocked for us. However, they come with their own set of challenges and intricacies. 🔑 Sequence Matters Take a look at these two sentences below. A small shift in word placement completely changes the meaning, demonstrating how sensitive language is to structure: I only follow a healthy lifestyle. 🏋️♀️ Only I follow a healthy lifestyle? 🤔 While the words are the same, their sequence creates entirely different messages. This is exactly why understanding language, both as humans and through models, is such a complex but fascinating task. 💡 Key takeaway: Even slight shifts in word order can lead to significant changes in meaning. The same is true for the design of language models—they need to account for these nuances! 🌟 #LanguageModels #AI #NaturalLanguageProcessing #MachineLearning #TechInsights #Innovation 🚀