Jing Liu @JLiu_Compuling
2nd year PhD student @CoML_ENS | Msc @LeuvenAi| ResMA @CLSRadboud| reverse engineer language acquisition using NN sites.google.com/view/jliucog/ Paris, France Joined June 2021-
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🎙️Slides from our @ISCAInterspeech tutorial “Speech Technology Meets Early Language Acquisition” (with Emmanuel Dupoux & Okko Räsänen) are online: zenodo.org/records/170182… We hope they’re useful for anyone interested in connecting speech tech and early language learning!
LLMs Get Lost in Multi-turn Conversation The cat is out of the bag. Pay attention, devs. This is one of the most common issues when building with LLMs today. Glad there is now paper to share insights. Here are my notes:
We are expecting🫄 A 3rd BabyLM👶, as a workshop @emnlpmeeting Kept: all New: Interaction (education, agentic) track Workshop papers More in 🧵
Our LLaMA Speech team at Meta GenAI is hiring Research Scientist Interns this year! Our research interest spans various aspects of speech LLMs and GenAI for audio/speech. If you are interested in, please DM with CV.
🎉 Excited to share our recent work: Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback 👉 Check out the paper: arxiv.org/abs/2501.12895 👉 Explore the code: github.com/yafuly/TPO 👉 View on Huggingface: huggingface.co/papers/2501.12…
For those trying to understand DeepSeeks Group Relative Policy Optimization (GRPO): GRPO is just PPO without a value function using monte carlo estimates of the advantage. So, study why PPO exists (lots of docs / writing on that) and understand that value functions are tricky…
🎓 Exciting HDR defense by Abdellah Fourtassi -@Adinseg! "How Machine Learning is Transforming the Science of Language Acquisition" 🗓️ Jan 17, 14:00 📍 Campus Saint-Charles, Salle des Séminaires Frumam Explore the impact of NLP on child language development! #LanguageAcquisition
🎉 Super happy to announce that a significant part of @LavechinMarvin's and my PhD research on modelling early language acquisition has finally been published in Developmental Science! 📄 doi.org/10.1111/desc.1… (OS version : onlinelibrary.wiley.com/share/author/9…) 🧵1/7
Don't do RAG Proposes cache-augmented generation (CAG) to eliminate retrieval latency and minimize retrieval errors. What is CAG? CAG aims to leverage the capabilities of long-context LLMs by preloading the LLM with all relevant docs in advance and precomputing the key-value…
Humans learn language from situated communicative interactions. And that makes all the difference! Find out why in our latest article in Computational Linguistics: doi.org/10.1162/coli_a… @UNamurCSFaculty @aibrussels @frsFNRS @FWOVlaanderen @MUHAI_org #CompLing #CxG #LLMs #NLProc
Humans learn language from situated communicative interactions. And that makes all the difference! Find out why in our latest article in Computational Linguistics: doi.org/10.1162/coli_a… @UNamurCSFaculty @aibrussels @frsFNRS @FWOVlaanderen @MUHAI_org #CompLing #CxG #LLMs #NLProc
Check out our new benchmark on Evaluating LMs as Synthetic Data Generators! Main findings: - LMs' ability to generate synthetic data varies - This is not necessarily correlated with problem solving ability - More data from cheaper models is often better than less from stronger
Check out our new benchmark on Evaluating LMs as Synthetic Data Generators! Main findings: - LMs' ability to generate synthetic data varies - This is not necessarily correlated with problem solving ability - More data from cheaper models is often better than less from stronger
I will teach Large Language Model Systems again in Spring 2025. (11868 for CMU folks) The course syllabus (tentative) is online at llmsystem.github.io/llmsystem2025s… CMU ppl are welcome to enroll. For others, I will release the materials online. Or apply to CMU LLM/GenAI certificate program
The workshop on Fine-grained Evaluation of VLMs (FINE) is approaching: Nov. 28 @unibz_news part of @AI_x_IA 2024. Together with the other OC members, @ffalchi, @BernardoMagnini, @AlexLenci1966, and Nicu Sebe, I look forward to the talks and the panel. 👉 sites.google.com/unitn.it/fine-…
Zero to Hero with the TRL learning link bomb 💣 TRL is a cornerstone of LLM post training and imo it's the default to learn. There are great alternatives like Unsloth, Axolotl, and AutoTrain. But if you want a daily drive that does experimentation to production, it's TRL. 🧵…
🔥Exploring o1-like Multimodal Reasoning🔥 🧠Insight-V🧠 scalably produces reasoning data and enhances the multimodal LLM reasoning capabilities. - Paper: arxiv.org/pdf/2411.14432 - Code: github.com/dongyh20/Insig… - Model @huggingface : huggingface.co/collections/TH… . Thanks @_akhaliq !
🔥Exploring o1-like Multimodal Reasoning🔥 🧠Insight-V🧠 scalably produces reasoning data and enhances the multimodal LLM reasoning capabilities. - Paper: arxiv.org/pdf/2411.14432 - Code: github.com/dongyh20/Insig… - Model @huggingface : huggingface.co/collections/TH… . Thanks @_akhaliq ! https://t.co/T0iEpiEs9e
Our key assumption? LLMs don’t actually exist at the level of discrete predictions, but in the gradient field of possibilities that sits behind each token. Anybody that tells you we understand what is going on inside an LLM during training is deluding you or themselves. They…
Our key assumption? LLMs don’t actually exist at the level of discrete predictions, but in the gradient field of possibilities that sits behind each token. Anybody that tells you we understand what is going on inside an LLM during training is deluding you or themselves. They… https://t.co/R3M0ZGSWCn
[CL] MemoryFormer: Minimize Transformer Computation by Removing Fully-Connected Layers N Ding, Y Tang, H Qin, Z Zhou… [Peking University & Huawei Noah’s Ark Lab] (2024) arxiv.org/abs/2411.12992
🚀 New Paper📜 When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training 🤯 RoPE is Broken because of... BFloat16! > Even if RoPE is computed in Float32 (like in Llama 3 and the current Transformers library), casting tensors to BFloat16 in…
Cosine similarity replaces softmax in transformers, enabling linear memory scaling for processing longer sequences, proposed in this paper. By decoupling the norm of the input matrices and using L2 normalization, Cottention allows for efficient computation of attention weights…

Prof. Asli Ozyurek @o... @ozyurek_a
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Prof. Asli Ozyurek @o... @ozyurek_a
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lindadrijvers.bskyapp... @lindadrijvers
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Multimodallang Dept @... @Multimodallang
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Abdellah Fourtassi @Adinseg
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Goodfire @GoodfireAI
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DeepSeek @deepseek_ai
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Yafu Li @yafuly
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