#languageModels - Twitter Hashtag

about 2 hours ago

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

retweetsxyz's tweet photo. https://t.co/JcQRKhfd8F for sale

#llm #llms #ai #languagemodels
#artificialintelligence #chatgpt #openai #grok #anthropic #xai https://t.co/rcoiM6zhIZ

0

21

Cuda Education @cudaeducation

2 days ago

language models https://t.co/ELkTFCBs6d #ai #languagemodels #llm #machinelearning #artificialintelligence #research #innovation #rnd #upskilling #gradientdescent #cudaeducation

1

0

14

Axiopistis Holdings LC @axiopistis

4 days ago

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

0

11

Kaynexis @fezatrack

7 days ago

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

0

28

Immanuel Trummer

@ImmanuelTrummer

8 days ago

✈️ Looking forward to an exciting #SIGMOD2026! 👇 Schedule below. 🇮🇳 See you soon in #Bengaluru! #Databases #LLMs #LanguageModels #DatabaseBenchmarking #QueryOptimization #QuantumComputing @lojil192574 @sigmod @SIGMODConf

ImmanuelTrummer's tweet photo. ✈️ Looking forward to an exciting #SIGMOD2026!
👇 Schedule below.
🇮🇳 See you soon in #Bengaluru!
#Databases #LLMs #LanguageModels #DatabaseBenchmarking #QueryOptimization #QuantumComputing
@lojil192574 @sigmod @SIGMODConf https://t.co/W5WRp1krHO

0

9

0

391

RelAItool @relaitool

13 days ago

Prompting in Polish outperforming English? That’s a wild reminder: AI reasoning isn’t language-neutral. The future of prompt strategy may be multilingual, not default-English. 🇵🇱🤖 #AI #PromptEngineering #LanguageModels https://t.co/0gKtFHG4NP

0

1

0

5

David @nova_agent945

15 days ago

Text generation is the last modality stuck with discrete tokens — 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. 🤔 #AI #LanguageModels

0

1

0

11

Justin ("Goju") Gottschlich

@j_gottschlich

15 days ago

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

1

0

65

David @LiDavid2002

17 days ago

📄 Paper: https://t.co/ATpyG6rYwn 💻 Code: https://t.co/Vad4bnNXjN 📢 Invitation post: https://t.co/1Ny00y2nHN Many thanks to the organizers for the invitation, @jdeschena, @ssahoo_, @zhihanyang_! 🙌 #ICML2026 #DiffusionModels #LanguageModels #GenerativeAI #MachineLearning

Discrete Diffusion Reading Group

@diffusion_llms

19 days ago

📢 May 18 (Mon): IDLM: Inverse-distilled Diffusion Language Models 🤔Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow inference, limiting practical use. 💡To 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. 🔧To 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. 📊As a result, experiments on multiple DLMs show that their method, Inverse-distilled Diffusion Language Models (IDLM), reduces the number of inference steps by 4×—64×, while preserving the teacher model’s 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

diffusion_llms's tweet photo. 📢 May 18 (Mon): IDLM: Inverse-distilled Diffusion Language Models

🤔Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow inference, limiting practical use.

💡To 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.

🔧To 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.

📊As a result, experiments on multiple DLMs show that their method, Inverse-distilled Diffusion Language Models (IDLM), reduces the number of inference steps by 4×—64×, while preserving the teacher model’s 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

2

36

13

11

10K

0

10

1

453

Justin ("Goju") Gottschlich

@j_gottschlich

18 days ago

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

0

1

0

90

Krishna Garigipati @kris_theaicoder

19 days ago

Building GLM-X: a graph-native cognitive architecture beyond transformers, now learning language from structured world knowledge. 👽🔥 #CognitiveAI #artificialintelligence #languagemodels #worldmodels

kris_theaicoder's tweet photo. Building GLM-X: a graph-native cognitive architecture beyond transformers, now learning language from structured world knowledge. 👽🔥

#CognitiveAI #artificialintelligence #languagemodels #worldmodels https://t.co/9oQUfoI4Qr

1

2

0

25

~~ Pooja ~~ @poojabnf

21 days ago

Claude for Legal Wes Roth explains Claude's prompt capabilities. #AIforLaw #LegalTech #Claude #LanguageModels #LawInnovation

0

16

GdyDigital @Gdy_Digital

22 days ago

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

0

25

KIE UEP @kie_uep

27 days ago

🎓 Ewelina Księżniak presented her paper “#Thesis Proposal: Comparing Human and Model Perception of Writing Style under Controlled Perturbations” at #EACL2026. ✍️ The research explores how humans and #LanguageModels perceive #WritingStyle. More: https://t.co/HvL0IVfJwO #NLP