Shruti Singh @ ACL 2024 @shruti_rsingh
Representation Learning for Scientific Literature | #NLProc | Fulbright fellow @yale | CS Ph.D. Student @iitgn | Past @daiictofficial shruti-singh.github.io India Joined August 2014-
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🌟 Join the @lingoiitgn as a PhD student! 🌟 Dive into cutting-edge NLP research, including Multilingual & Multimodal LLMs and SLMs.💻 Access SOTA computing & GPUs🔥 Strong funding & industry connections 📈 Proven publication record 🚀 Shape impactful products with us! #NLP #LLM
🌟 Join the @lingoiitgn as a PhD student! 🌟 Dive into cutting-edge NLP research, including Multilingual & Multimodal LLMs and SLMs.💻 Access SOTA computing & GPUs🔥 Strong funding & industry connections 📈 Proven publication record 🚀 Shape impactful products with us! #NLP #LLM
For vector search, practitioners kinda know that for small corpora, don't bother with HNSW indexing, just brute-force it. However, guidance is mostly hand wavy... until now. I ran some experiments for you on BEIR and wrote it up. arxiv.org/abs/2409.06464 You're welcome.
It’s Sunday morning we have some time with the coffee so let me tell you about some of our recent surprising journey in synthetic data and small language models. This post is prompted by the coming release of an instant, in-browser model called SmolLM360 (link at the end) The…
Introducing SciRIFF, a toolkit to enhance LLM instruction-following over scientific literature. 137k expert demonstrations in 5 categories: IE, summarization, QA, entailment, and classification; models up to 70b and code to science-tune your checkpoints included! Read more in 🧵:
📣 Please make note of the #FSTTCS2024 call for papers: three weeks to the abstract submission deadline! The PC chairs have already lined up a phenomenal set of invited talks. 🤩 Do help us spread the word and plan to submit and/or attend. Links in the next tweet. (1/2)
Meta presents Iterative Reasoning Preference Optimization Increasing accuracy for Llama-2-70B-Chat: - 55.6% -> 81.6% on GSM8K - 12.5% -> 20.8% on MATH - 77.8% -> 86.7% on ARC-Challenge arxiv.org/abs/2404.19733
New paper from @RekaAILabs 🔥 (yes an actual paper). This time we're releasing part of our internal evals which we call Vibe-Eval 😃 This comprises of a hard set which imo is pretty challenging for frontier models today. The fun part here is that we constructed it by trying to…
Meta presents Better & Faster Large Language Models via Multi-token Prediction - training language models to predict multiple future tokens at once results in higher sample efficiency - up to 3x faster at inference arxiv.org/abs/2404.19737
📢 Releasing TRI's open-source Mamba-7B trained on 1.2T tokens of RefinedWeb! Mamba-7B is the largest fully recurrent Mamba model trained and is a state-of-the-art recurrent LLM. 🚀🚀🚀 huggingface.co/TRI-ML/mamba-7…
How to make LMs learn an abstract language representation from learning 2 languages on the same concept? A shared token helps, sharing output language helps more arxiv.org/abs/2404.12444 Tianze Hua @tianyunnn @Brown_NLP (no, I won't put it at the top hype-seekers)
Presenting a method for training models from SCRATCH using LoRA: 💡20x reduction in communication 💡3x savings in memory - Find out more: minyoungg.github.io/LTE/ - Code available to try out - Scaling to larger models ongoing - arxiv.org/pdf/2402.16828… led by Jacob Huh!
Air pollution is a major problem in India potentially harming life expectancy by several years in India. Come help us! We have a couple of JRF openings in our lab @cse_iitgn @iitgn 1. Air Quality exposure estimation and forecasting: drive.google.com/file/d/16P-Y8D… 2. Active…
Attention cognitive science and neuroscience enthusiasts! Deadline extension Alert! The application deadline for IITGN’s MSc programme in Cognitive Science has now been extended till Jan 24. Don’t miss out on this opportunity and apply now! Click here : lnkd.in/ddjA2QUK
#GandhiPedia is getting inaugurated today. A great moment for us to celebrate the technology and collaborative efforts from diverse set of experts. A huge shout out for @IITKgp, @iitgn, @ncsmgoi teams.
**Test of Time** Distributed Representations of Words and Phrases and their Compositionality **Outstanding Main Track Papers** Privacy Auditing with One (1) Training Run Are Emergent Abilities of Large Language Models a Mirage?
Excited to share our work on "Benchmarking Generation and Evaluation Capabilities of LLMs for Instruction Controllable Summarization"! As LLMs excel in generic summarization, we must explore more complex task settings.🧵 arxiv.org/abs/2311.09184 Equal contribution @alexfabbri4
Excited to announce the Workshop on 👷🛠️🤖 Instruction Tuning & Instruction Following 👷🛠️🤖 (ITIF) at #NeurIPS2023! 📅 Join us on Dec 15 in New Orleans 🛠️ Submit by Oct 1 👷 See speaker lineup 🔗an-instructive-workshop.github.io 1/
A 5 day event on LLMs. Line up looks interesting. (All talks are streamed live on YouTube) simons.berkeley.edu/workshops/larg… #machinelearning
1/13 🧵💡 Ever wondered how to handle token limitations of LLMs? Here's one strategy of the "map-reduce" technique implemented in @LangChainAI 🦜🔗 Let's deep dive! @hwchase17 's your PR is under review again😎
Adam, a 9-yr old optimizer, is the go-to for training LLMs (eg, GPT-3, OPT, LLAMA). Introducing Sophia, a new optimizer that is 2x faster than Adam on LLMs. Just a few more lines of code could cut your costs from $2M to $1M (if scaling laws hold). arxiv.org/abs/2305.14342 🧵⬇️

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