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If you're interested in memory efficient pre-trained LMs chech out Huiyin's new paper on vocabulary and tokenization independent Hashformer models (to appear at #EMNLP2022) 👇

The generative AI race has a dirty secret. Dr Nafise Sadat Moosavi, from Sheffield Comp Sci comments on the need to make large language models like the ones used by Google and Microsoft more efficient. wired.co.uk/article/the-ge…

Efficient NLP methods - an up-to-date survey, to appear in TACL. We cover efficiency wrt: * Data * Model design * Pre-training * Fine-tuning * Inference & compression * Hardware utilization * Evaluation * Model selection This was a blast to co-produce! arxiv.org/abs/2209.00099

✂️ Pruning Parameters = Pruning Hallucinations🌪️! Our latest paper reveals the sweet spot: up to 50% pruned, the more you prune, the lower hallucination risk. It's a buy one get one free for #LLMs. Nikos Aletras George Chrysostomou Miles Williams 😎 arxiv.org/abs/2311.09335

#PhDstudentship available in #LLMs at Sheffield NLP, one of the UK's largest #NLP research centres Sheffield Comp Sci * 3.5yrs tuition waiver & stipend * knowledge editing, model compression, or your proposal! DM for details! Apply now: shorturl.at/xCM36 #PhD #AI #PhDposition

A fully-funded 3.5y scholarship (UK/Home applicants) for a PhD in #NLProc is available! Broad topic: efficient and robust alignment of LLMs. Application deadline: Jun 10th More info: findaphd.com/phds/project/e…

Job opportunity Sheffield NLP 🚨: I'm looking for a #postdoc (24 months) in #NLProc The (very) broad topic is on addressing LLMs limitations (e.g. hallucinations, "reasoning", interpretability etc.) If you are interested drop me an email or DM Apply: jobs.ac.uk/job/DHP918/res…

🔍 Lighter is better! 📚 Our latest TACL paper reveals some fascinating aha moments for #LLMs. Check it out! 🚀 #NLProc

We're looking for a #PhD student to work on multimodal LLMs. This is a fully-funded scholarhsip (including stipend), open to home and international candidates. Deadline: 29/1/2025 Please spread the work! #nlproc

Synthetic calibration data (for pruning and quantization) generated by the LLM itself is a better approx of the pre-training data dist than "external" data. Really cool work by Miles (Miles Williams) and George (George Chrysostomou) to be presented at #NAACL2025 Link to the paper 👇

Gutted to miss #NAACL2025 😭 but Miles Miles Williams will be there presenting the following papers: 📄 Main: Self-Calibration for Language Model Quantization and Pruning 📄 RepL4NLP: Vocabulary-Level Memory Efficiency for LM Fine-Tuning Check them out!