#Tag · bonfire.cafe

🔬 The accepted papers span a broad range of current #NLP research, including 𝗱𝗲𝗻𝘀𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹, 𝗯𝗶𝗮𝘀 𝗮𝗻𝗱 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗟𝗟𝗠𝘀, 𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗮𝗻𝗱 𝗲𝗺𝗼𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗶𝘁𝘆, 𝗺𝗼𝗱𝗲𝗹 𝘂𝗻𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗽𝗲𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄 𝗮𝗻𝗱 𝘀𝗰𝗵𝗼𝗹𝗮𝗿𝗹𝘆 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸, and 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗱𝗲 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻.

1️⃣ 𝗚𝗥𝗜𝗧𝗛𝗼𝗽𝗽𝗲𝗿: 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻-𝗙𝗿𝗲𝗲 𝗠𝘂𝗹𝘁𝗶-𝗛𝗼𝗽 𝗗𝗲𝗻𝘀𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
Justus-Jonas Erker, Nils Reimers, Iryna Gurevych

2️⃣ 𝗛𝗼𝘄 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗦𝗵𝗮𝗽𝗲𝘀 𝗕𝗶𝗮𝘀 𝗶𝗻 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀
Federico Marcuzzi, Xuefei Ning, Roy Schwartz, Iryna Gurevych

Andreas Wagner boosted

UKP Lab

@UKPLab@sigmoid.social · 2 weeks ago

🎉 𝗨𝗞𝗣 𝗟𝗮𝗯 𝗮𝘁 𝗘𝗔𝗖𝗟 𝟮𝟬𝟮𝟲
🌍 We are pleased to share that researchers from the UKP Lab and their collaborators will present 𝟵 𝗽𝗮𝗽𝗲𝗿𝘀 𝗮𝘁 𝘁𝗵𝗲 𝗠𝗮𝗶𝗻 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 at the #EACL2026, one of the leading international conferences in #NLP and #CL, bringing together research from across Europe and beyond.

UKP Lab

@UKPLab@sigmoid.social · 2 weeks ago

UKP Lab

@UKPLab@sigmoid.social replied · 2 weeks ago

UKP Lab

@UKPLab@sigmoid.social · 2 weeks ago

Hacker News

@h4ckernews@mastodon.social · 4 weeks ago

The Illustrated Transformer

https://jalammar.github.io/illustrated-transformer/

#HackerNews #IllustratedTransformer #NLP #AI #MachineLearning #Visualization

The Illustrated Transformer

Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese Watch: MIT’s Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford, Harvard, MIT, Princeton, CMU and others Update: This post has now become a book! Check out LLM-book.com which contains (Chapter 3) an updated and expanded version of this post speaking about the latest Transformer models and how they've evolved in the seven years since the original Transformer (like Multi-Query Attention and RoPE Positional embeddings). In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions. The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter. 2025 Update: We’ve built a free short course that brings the contents of this post up-to-date with animations: A High-Level Look Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.

Hacker News

@h4ckernews@mastodon.social · last month

Speech and Language Processing (3rd ed. draft)

https://web.stanford.edu/~jurafsky/slp3/

#HackerNews #SpeechProcessing #LanguageProcessing #NLP #StanfordJurafsky #DraftEdition

Esther Payne :bisexual_flag: boosted

Aaron

@hosford42@techhub.social · 2 months ago

If you want a specific example of why many researchers in machine learning and natural language processing find the idea that LLMs like ChatGPT or Claude are "intelligent" or "conscious" is laughable, this article describes one:

https://news.mit.edu/2025/shortcoming-makes-llms-less-reliable-1126

#LLM
#ChatGPT
#Claude
#MachineLearning
#NaturalLanguageProcessing
#ML
#AI
#NLP

Aaron

@hosford42@techhub.social · 2 months ago

https://news.mit.edu/2025/shortcoming-makes-llms-less-reliable-1126

#LLM
#ChatGPT
#Claude
#MachineLearning
#NaturalLanguageProcessing
#ML
#AI
#NLP

Andreas Wagner boosted

Elen Le Foll 🇫🇷 🇬🇧 🇩🇪

@ElenLeFoll@fediscience.org · 2 months ago

We began day 2 of our Large Language Models (LLM) for linguistics research workshop @UniKoeln with a fascinating keynote by Charlotte Pouw on "Interpreting models for speech generation and understanding using methods from #psycholinguistics". Charlotte shared insights from two studies from her #PhD research. Check out the papers: https://aclanthology.org/2025.conll-1.9/ and https://direct.mit.edu/coli/article/50/4/1557/123790. 7/🧵

#LLM #NLP #linguistics

2 media

Charlotte presenting a slide with plots entitled The Role of Data Exposure

Elen Le Foll 🇫🇷 🇬🇧 🇩🇪

@ElenLeFoll@fediscience.org · 2 months ago

Oups, I nearly forgot to present our own poster... Luckily, Tiziana Ilie, M.A. student and first author on this project, did a grand job presenting our work on how DeepL deals with pronomial pickups of the hybrid noun "Mädchen". For this study, we used the #OpenSource model OLMo-2-7B to generate our experimental stimuli. Our abstract is here: https://paths.to/m%C3%A4dchen-poster and we will have a preprint out soon so watch this space...

#MachineTranslation #LLM #linguistics

Tiziana and I presenting our poster about "Genus-sexus congruence in machine translation: DeepL's pronominal resumption of 'das Mädchen'"

Elen Le Foll 🇫🇷 🇬🇧 🇩🇪

@ElenLeFoll@fediscience.org replied · 2 months ago

#LLM #NLP #linguistics

2 media

Andreas Wagner boosted

UKP Lab

@UKPLab@sigmoid.social · 2 months ago

The day featured well-prepared talks, thoughtful questions, and lively exchanges across topics.

A big thank you to Yongxin Huang, our thesis coordinator, for guiding this cohort through the process, and to all supervisors for their continuous support throughout the semester.

We wish all students the best of luck as they finalize their submissions by the end of this month! ✨

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#UKPLab #ThesisDay #NLP #NLProc

UKP Lab

@UKPLab@sigmoid.social · 2 months ago

The day featured well-prepared talks, thoughtful questions, and lively exchanges across topics.

A big thank you to Yongxin Huang, our thesis coordinator, for guiding this cohort through the process, and to all supervisors for their continuous support throughout the semester.

We wish all students the best of luck as they finalize their submissions by the end of this month! ✨

(2/2)

#UKPLab #ThesisDay #NLP #NLProc

Andreas Wagner boosted

DH Lab IEG

@DHLab_IEG@fedihum.org · 2 months ago

Can we trust general-purpose LLMs for rigorous academic work? According to @SarahOberbichler from the #DHlab_IEG, research integrity demands specialized AI models, not general-purpose LLMs when using AI as an analysis tool.

Join her upcoming talk "Argument Mining in News Media: Tailoring Models and Methods for Responsible Application" on Nov 26th at CESR (University of Tours) or online
➡️ https://prima.hypotheses.org/3181

#DigitalHumanities #NLP #ArgumentMining #ResearchIntegrity #AI #LLMs #Histodons #DH

Infoflyer for the announced event. Left: Pictures of Newspaper articles. Right: Text with announcement. The text reads: "Humanites Numeriques Tourangelles, Mercredi 26 novembre 2025, 17h-19h - Tours, CESR - Salle Rapin et en visioconférence. #9 Argument Mining in News Media: Tailoring Models and Methods for Responsible Application, Sarah Oberbichler - Digital Humanities Lab, Leibniz Institute of European History."

Andreas Wagner boosted

DH Lab IEG

@DHLab_IEG@fedihum.org · 3 months ago

Promovierende aufgepasst: Ihr seid euch unsicher, ob Topic Modeling das richtige für eure computergestützten Auswertungen ist? Oder doch lieber (nicht) Netzwerkanalyse? Und welche Alternativen gäbe es? Am 17. 11. (16-17:30 Uhr) habt Ihr die Gelegenheit, euch darüber mit unserer Kollegin Cindarella Petz ( @cprog7) im Rahmen des HERMES-Netzwerktreffens (online) auszutauschen.

Infos / Anmeldung: https://hermes-hub.de/aktuelles/events/netzwerktreffen-2025-11-17.html

#DHLab_IEG #HERMES #DigitalHumanities #NLP #TextMining #NetzwerkAnalyse #HNR

DH Lab IEG

@DHLab_IEG@fedihum.org · 2 months ago

#DigitalHumanities #NLP #ArgumentMining #ResearchIntegrity #AI #LLMs #Histodons #DH

Hacker News

@h4ckernews@mastodon.social · 2 months ago

Tiny Diffusion – A character-level text diffusion model from scratch

https://github.com/nathan-barry/tiny-diffusion

#HackerNews #TinyDiffusion #TextModel #DiffusionAI #MachineLearning #NLP

DH Lab IEG

@DHLab_IEG@fedihum.org · 3 months ago

Infos / Anmeldung: https://hermes-hub.de/aktuelles/events/netzwerktreffen-2025-11-17.html

#DHLab_IEG #HERMES #DigitalHumanities #NLP #TextMining #NetzwerkAnalyse #HNR

Hacker News

@h4ckernews@mastodon.social · 3 months ago

Word2vec-style vector arithmetic on docs embeddings

https://technicalwriting.dev/embeddings/arithmetic/index.html

#HackerNews #Word2vec-style #vector #arithmetic #on #docs #embeddings #Word2vec #vectorarithmetic #docsembeddings #NLP #MachineLearning

word2vec-style vector arithmetic on docs embeddings

UKP Lab

@UKPLab@sigmoid.social · 3 months ago

🔗 Learn more:
• Official website → https://sbert.net/
• Original paper → https://aclanthology.org/D19-1410.pdf
• GitHub repository → https://github.com/UKPLab/sentence-transformers

📰 Read the full announcements:
TU Darmstadt Press Release
→ https://www.tu-darmstadt.de/universitaet/aktuelles_meldungen/einzelansicht_528832.de.jsp

Hugging Face Blog Post
→ https://huggingface.co/blog/sentence-transformers-joins-hf

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#UKPLab #HuggingFace #SentenceTransformers #NLP #AIresearch #OpenSource 🚀

Sentence Transformers is joining Hugging Face!

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

TU Darmstadt

So versteht KI ganze Sätze

Das Ubiquitous Knowledge Processing (UKP) Lab der Technischen Universität Darmstadt hat die Wartung und Weiterentwicklung von Sentence Transformers, einer der weltweit meistgenutzten Open-Source-Bibliotheken für semantische Embeddings, offiziell an die Plattform Hugging Face übertragen. Die Open-Source-Software ging 2019 aus der Forschung am UKP Lab hervor und hat sich seither als eine der wichtigsten Ressourcen für Künstliche Intelligenz (KI) in der Sprachverarbeitung (Natural Language Processing, kurz NLP) etabliert. Sie ermöglicht es, ganze Sätze so darzustellen, dass Computer deren Bedeutung erfassen und vergleichen können.

GitHub

GitHub - huggingface/sentence-transformers: State-of-the-Art Text Embeddings

State-of-the-Art Text Embeddings. Contribute to huggingface/sentence-transformers development by creating an account on GitHub.

View (PDF)

Kathy Reid

@KathyReid@aus.social · 4 months ago

🚨 #NLP SHARED TASK 🚨

Use Mozilla Common Voice Spontaneous #Speech datasets to train #ASR #SpeechRecognition models that work for conversational speech on 21 under-represented languages.

📆 Dataset release 1 Dec
📆 Submissions 8 Dec
💰 $USD 11k prize pool !!!

Boosts appreciated ❤️

https://community.mozilladatacollective.com/shared-task-mozilla-common-voice-spontaneous-speech-asr?utm_source=mastodon&utm_campaign=kathysharedtask

Mozilla Data Collective

Shared Task: Mozilla Common Voice Spontaneous Speech ASR

Overview Automatic speech recognition (ASR) has come a long way – but most systems are still trained on polished, read-aloud speech. So we set out to build a model that can handle the messy, beautiful reality of spontaneous responses and languages long ignored by mainstream tech. We’re raising the standards