Top Tweets for #languageModels
https://t.co/JcQRKhfd8F for sale
#llm #llms #ai #languagemodels
#artificialintelligence #chatgpt #openai #grok #anthropic #xai

language models https://t.co/ELkTFCBs6d #ai #languagemodels #llm #machinelearning #artificialintelligence #research #innovation #rnd #upskilling #gradientdescent #cudaeducation
Liquid AI unveils 8B-A1B MoE trained on 38T. A giant Mixture-of-Experts model pushing scalable, efficient inference. Could redefine large-scale NLP at cost with performance. #AI #ML #DeepLearning #MoE #LanguageModels
Instead of treating input-output pairs as isolated points, CREDO turns learning signals into reusable certified theory: rules, evidence, normal forms, quotient invariance, proof-DAG reuse and GPU-executable factor graphs.
#LanguageModels #AI #Verification
https://t.co/4hQHNRDCOU
โ๏ธ Looking forward to an exciting #SIGMOD2026!
๐ Schedule below.
๐ฎ๐ณ See you soon in #Bengaluru!
#Databases #LLMs #LanguageModels #DatabaseBenchmarking #QueryOptimization #QuantumComputing
@lojil192574 @sigmod @SIGMODConf

Prompting in Polish outperforming English? Thatโs a wild reminder: AI reasoning isnโt language-neutral. The future of prompt strategy may be multilingual, not default-English. ๐ต๐ฑ๐ค #AI #PromptEngineering #LanguageModels
https://t.co/0gKtFHG4NP
Text generation is the last modality stuck with discrete tokens โ and it's not because it's better. It's historical inertia. Image gen moved to continuous. Audio gen moved. Text is next. The question is who breaks the spell first. ๐ค #AI #LanguageModels
Using language models in isolation is the wrong direction. True progress lies in iterative refinement towards a fundamentally correct direction, not just precision. #GojuTechTalk #GojuTrek #AIResearch #LanguageModels
๐ Paper: https://t.co/ATpyG6rYwn
๐ป Code: https://t.co/Vad4bnNXjN
๐ข Invitation post: https://t.co/1Ny00y2nHN
Many thanks to the organizers for the invitation,
@jdeschena, @ssahoo_, @zhihanyang_! ๐
#ICML2026 #DiffusionModels #LanguageModels #GenerativeAI #MachineLearning
๐ข May 18 (Mon): IDLM: Inverse-distilled Diffusion Language Models
๐คDiffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow inference, limiting practical use.
๐กTo address this, the authors extend Inverse Distillation, a technique originally developed to accelerate continuous diffusion models, to the discrete setting. However, this extension introduces both theoretical and practical challenges.
๐งTo overcome these challenges, the authors first provide a theoretical result demonstrating that their inverse formulation admits a unique solution, thereby ensuring valid optimization. They then introduce gradient-stable relaxations to support effective training.
๐As a result, experiments on multiple DLMs show that their method, Inverse-distilled Diffusion Language Models (IDLM), reduces the number of inference steps by 4รโ64ร, while preserving the teacher modelโs entropy and generative perplexity.
This Monday, David Li (https://t.co/8geY1UUQ72) and Nikita Gushchin (https://t.co/L4nc4Gz7ZC) will present their jointly led paper, which was recently accepted at ICML 2026.
Collaborators of this work include: Dmitry Abulkhanov (@dabulkhanov_), Eric Moulines (https://t.co/tK9ctSzw0r), Ivan Oseledets (@oseledetsivan), Maxim Panov (@maxim_panov), Alexander Korotin (https://t.co/RTSk7HAc3v)
Paper link: https://t.co/L8MniD5GBu

Language models are stochastic in outcome / output. This makes improving them using training output nearly impossible. It's also why they are plateauing. #LanguageModels #LLM #Anthropic #OpenAI #AI #StochasticAI #TechExplained
Building GLM-X: a graph-native cognitive architecture beyond transformers, now learning language from structured world knowledge. ๐ฝ๐ฅ
#CognitiveAI #artificialintelligence #languagemodels #worldmodels

Claude for Legal
Wes Roth explains Claude's prompt capabilities. #AIforLaw #LegalTech #Claude #LanguageModels #LawInnovation
A new 103B-parameter medical language model, AntAngelMed, utilizes a 1/32 activation-ratio Mixture-of-Experts architecture..
#AI #MedicalAI #Languagemodels
https://t.co/RbRTex1W7i
๐ Ewelina Ksiฤลผniak presented her paper โ#Thesis Proposal: Comparing Human and Model Perception of Writing Style under Controlled Perturbationsโ at #EACL2026.
โ๏ธ The research explores how humans and #LanguageModels perceive #WritingStyle.
More: https://t.co/HvL0IVfJwO #NLP

Training AI Models on Core Values Improves Their Ethical Behavior, Study Finds
https://t.co/44OwwuNV8x
#aiethics #ainews #aisafety
#aialignment #aitrainingmethods #aivalues #aiethics #anthropic #languagemodels #machinelearning
ProgramBench: Can Language Models Rebuild Programs From Scratch?
๐ฅ John Yang, Kilian Lieret, Jeffrey Ma et al.
#AIResearch #MachineLearning #LanguageModels #SoftwareEngineering
๐ https://t.co/xAAqkqJ6R9

CL-bench Life: Can Language Models Learn from Real-Life Context?
๐ฅ Shihan Dou, Yujiong Shen, Chenhao Huang et al.
#AIResearch #NLP #LanguageModels #Benchmarking
๐ https://t.co/C21fLlILkY

language models https://t.co/KJPql10HII #ai #languagemodels #llm #machinelearning #artificialintelligence #research #innovation #rnd #upskilling #gradientdescent #cudaeducation
What caused ChatGPT to start using goblin references in its responses?
#ChatGPT #AI #ArtificialIntelligence #MachineLearning #OpenAI #GPT5 #AIExplained #TechNews #AIFacts #GenerativeAI #LanguageModels #AIResearch #DigitalTrends
https://t.co/d3r4saUHVi
@ykilcher's TiDAR model uses diffusion for fast token drafts, then autoregression for quality. The key is leveraging "free compute density" during AR inference. Made with @cluencyai
#LanguageModels #AI #MachineLearning #DeepLearning

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