Daria Vazhenina

📚 Just another domain-specific MT corpus? No! 🎵 Introducing the KpopMT dataset, designed to tackle the challenges of translating a unique language system emerging from a vibrant social group. Here's what sets it apart: 🔹 Familiar domain (chat) but with unique expressions 🔹 Rich examples featuring group-specific terminologies 🔹 Comprehensive terminology tagging with detailed types Comes with a parallel test set + termbase + additional monolingual data! To appear at LoresMT@ACL: https://lnkd.in/d4WR79c9

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💥💥💥 LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering Qingfei Zhao, Ruobing Wang, Yukuo Cen, Daren Zha, Shicheng Tan, Yuxiao Dong, Jie Tang Abstract Long-Context Question Answering (LCQA), a challenging task, aims to reason over long-context documents to yield accurate answers to questions. Existing long-context Large Language Models (LLMs) for LCQA often struggle with the "lost in the middle" issue. Retrieval-Augmented Generation (RAG) mitigates this issue by providing external factual evidence. However, its chunking strategy disrupts the global long-context information, and its low-quality retrieval in long contexts hinders LLMs from identifying effective factual details due to substantial noise. To this end, we propose LongRAG, a general, dual-perspective, and robust LLM-based RAG system paradigm for LCQA to enhance RAG's understanding of complex long-context knowledge (i.e., global information and factual details). We design LongRAG as a plug-and-play paradigm, facilitating adaptation to various domains and LLMs. Extensive experiments on three multi-hop datasets demonstrate that LongRAG significantly outperforms long-context LLMs (up by 6.94%), advanced RAG (up by 6.16%), and Vanilla RAG (up by 17.25%). Furthermore, we conduct quantitative ablation studies and multi-dimensional analyses, highlighting the effectiveness of the system's components and fine-tuning strategies. 👉 https://lnkd.in/d7PaajBQ #machinelearning

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Building a Japanese NLP Parser with Laravel, Vue.js, and Python 💻 皆さん、こんにちは！👋 As part of my journey to enhance my language skills and dive deeper into Natural Language Processing (NLP), I’ve just completed a project called Japanese NLP Parser. This project helps break down Japanese sentences into morphemes — the smallest units of meaning in a language. Since Japanese is written without spaces between words, parsing sentences is an essential first step in any NLP task. This parser simplifies Japanese sentence structures to improve language learning, translation tools, and chatbots. 🔧 Tech Stack: ✅ Laravel for the backend ✅ Vue.js for the frontend ✅ Python (SudachiPy) for Japanese text parsing 🌟 Features I’ve implemented: 🔸 Flashcards to help users learn new words and their meanings 🔸 Quiz Mode with random questions and multiple-choice answers 🔸 Shareable Results to let users share their quiz achievements The Quiz Mode also includes: ✔️ Randomized questions ✔️ Different question types (dictionary form, reading form, part of speech) ✔️ Timer for each question to enhance engagement 🚀 The application is now live and accessible online! Feel free to try it out: https://lnkd.in/gSPBGggY 📂 GitHub Repository: If you're curious about the code behind this project, check out the repo here: https://lnkd.in/gi8QHW3Z 💬 Why this project? I’ve always been fascinated by Japanese culture, language, and technology. As I prepare to build a career in Japan, this project not only helps me improve my Japanese skills but also strengthens my knowledge in NLP, web development, and interactive UI design. 📝 Next steps: In future updates, I plan to add: 🔹 Learning Mode to track user progress 🔹 Custom quizzes for vocabulary practice 🔹 Progress tracking and achievements もし興味があれば、ぜひ試してみてください！😊 Feel free to reach out if you'd like to discuss more about NLP, Japanese language learning, or web development. I'd love to connect and share ideas! #NLP #MachineLearning #Laravel #VueJS #Python #JapaneseLanguage #WebDevelopment #SudachiPy #LearningJourney #CareerInJapan #FullStackDevelopment #GitHub #LinkedInSharing

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Huge release by Daekeun Kim and Hyo (HK) Choi from our AI GBB team in Korea, sharing Korean language benchmarks for our latest Azure OpenAI model, gpt-4o-mini - like gpt-4o, the model excels on Korean language tasks, in this case mini completely eclipses gpt-35-turbo and nearly achieves gpt-4-turbo performance. The benchmarks and code are open source, extensible beyond our Azure models, so we encourage our customers and the larger community to try it out and even fork/share your own findings - and please let me in the comments what other Asia languages beyond Korean you may be looking to evaluate with your language models.

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Computing high-degree polynomial gradients in memory https://lnkd.in/gUWXbXDV

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💥💥💥 GUI Agents with Foundation Models: A Comprehensive Survey Shuai Wang, Weiwen Liu, Jingxuan Chen, Weinan Gan, Xingshan Zeng, Shuai Yu, Xinlong Hao, Kun Shao, Yasheng Wang, Ruiming Tang Abstract Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), facilitate intelligent agents being capable of performing complex tasks. By leveraging the ability of (M)LLMs to process and interpret Graphical User Interfaces (GUIs), these agents can autonomously execute user instructions by simulating human-like interactions such as clicking and typing. This survey consolidates recent research on (M)LLM-based GUI agents, highlighting key innovations in data, frameworks, and applications. We begin by discussing representative datasets and benchmarks. Next, we summarize a unified framework that captures the essential components used in prior research, accompanied by a taxonomy. Additionally, we explore commercial applications of (M)LLM-based GUI agents. Drawing from existing work, we identify several key challenges and propose future research directions. We hope this paper will inspire further developments in the field of (M)LLM-based GUI agents. 👉 https://lnkd.in/dtCrG5DB #machinelearning

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💥💥💥 Instruction-Following Pruning for Large Language Models Bairu Hou, Qibin Chen, Jianyu Wang, Guoli Yin, Chong Wang, Nan Du, Ruoming Pang, Shiyu Chang, Tao Lei Abstract With the rapid scaling of large language models (LLMs), structured pruning has become a widely used technique to learn efficient, smaller models from larger ones, delivering superior performance compared to training similarly sized models from scratch. In this paper, we move beyond the traditional static pruning approach of determining a fixed pruning mask for a model, and propose a dynamic approach to structured pruning. In our method, the pruning mask is input-dependent and adapts dynamically based on the information described in a user instruction. Our approach, termed "instruction-following pruning", introduces a sparse mask predictor that takes the user instruction as input and dynamically selects the most relevant model parameters for the given task. To identify and activate effective parameters, we jointly optimize the sparse mask predictor and the LLM, leveraging both instruction-following data and the pre-training corpus. Experimental results demonstrate the effectiveness of our approach on a wide range of evaluation benchmarks. For example, our 3B activated model improves over the 3B dense model by 5-8 points of absolute margin on domains such as math and coding, and rivals the performance of a 9B model. 👉 https://lnkd.in/dt8AnGYy #machinelearning

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Good writeup from InfluxData intern Xiangpeng Hao on "How Good is Parquet for Wide Tables (Machine Learning Workloads) Really?" This has some great tests along with concrete numbers: https://lnkd.in/eRaP4vU4 Part of the ongoing debate and design discussions on file formats for analytic databases.

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Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng, Joyce Jiyoung Whang Abstract Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more challenging scenario in which even the "relevant" documents may contain misleading or incorrect information, causing conflict among the retrieved documents and thereby negatively influencing model decisions as noise. We observe that existing LMs are highly brittle to the presence of conflicting information in both the fine-tuning and in-context few-shot learning scenarios. We propose approaches for handling knowledge conflicts among retrieved documents by explicitly fine-tuning a discriminator or prompting GPT-3.5 to elicit its discriminative capability. Our empirical results on open-domain QA show that these approaches significantly enhance model robustness. We also provide our findings on incorporating the fine-tuned discriminator's decision into the in-context learning process, proposing a way to exploit the benefits of two disparate learning schemes. Alongside our findings, we provide MacNoise, a machine-generated, conflict-induced dataset to further encourage research in this direction. 👉 https://lnkd.in/dRk_uage #machinelearning

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🌟 Excited to share our latest work this week at #ICASSP2024 in Seoul 🇰🇷! Come by on Thursday at 8:20 am in Poster Zone 6A for the presentation of "Multilingual DistilWhisper: Efficient Distillation of Multi-task Speech Models via Language-Specific Experts". We all love Whisper (99 languages, multitask, and multilingual), but it's large version is slow in inference and smaller ones underperforms in multilingual settings! 😞 🚀 Introducing our solution: Multilingual DistilWhisper! 🌍🔊 This approach bridges the performance gap in ASR for target languages while preserving the multitask and multilingual capabilities of the original model. How? Through lightweight modular ASR fine-tuning using language-specific experts and knowledge distillation from a larger whisper (whisper-large-v2). 📈 Our results speak volumes! Our approach outperforms standard fine-tuning or LoRA adapters, enhancing performance in targeted languages for both in- and out-of-domain test sets, with minimal parameter overhead at inference. 💡And the best part? It's data-light! Boosting performance by ~35% with just 14h of training data is just the beginning. We've shown that even with as little as 4h of data, relevant improvements are possible. Check out our paper, code and model weights for more insights: 📄 Paper: https://lnkd.in/dpHi4By9 💻 Code: https://lnkd.in/dAJC55qu 🤗 Model weights: https://lnkd.in/duqe__hw Current languages covered include ca, bg, cs, fi, gl, hi, hu, pl, ro, sk, ta, th, uk. Plus, it's super easy to train new experts! 🚀 This work was done as part of my internship last year at NAVER LABS Europe, along with this amazing team that taught me so much Marcely Zanon Boito Caroline Brun Vassilina Nikoulina.

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✨ 𝐋𝐨𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐯𝐬. 𝐑𝐀𝐆 𝐟𝐨𝐫 𝐋𝐋𝐌𝐬 🧠 Did you know that extending the memory of Large Language Models (LLMs) and using retrievers to access relevant information are two main strategies for enhancing their performance? 💡 This recent paper revisits studies on these strategies, highlighting key insights and discrepancies. 🔹Extending the memory (Long Context, LC) generally outperforms quickly accessing relevant information (Retrieval-Augmented Generation, RAG) in question-answering benchmarks. 🔹RAG has advantages in dialogue-based and general question queries. The paper underscores the trade-offs between these strategies, offering guidance for future optimization of LLMs with external knowledge sources. Paper https://lnkd.in/djQKhcnw #ai #llms #RAG #genai

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Developed a web appà AI-Powered Job Search & Auto-Apply Web App This web app will: ✅ Analyze a user's profile (resume, skills) ✅ Find relevant jobs on LinkedIn, Naukri, and Indeed ✅ Provide matches & allow one-click applications Please find the code repo here(Node JS, OpenAI API, Expo etc) https://lnkd.in/gutsjjZr Built a demand forecasting model prototype using a historic sales dataset . Used ML techniques-Scikit-learn,MinMaxScaler for scale range, LSTM(Long Short-Term Memory) model for training, testing & reshaping data ,python& integrate open-source LLM -Llama 3.1 to get demand forecasting prediction. We can leverage such kind of models & techniques for accurate inventory management & sales optimization for large scale real time projects esp.for retail domain. 2)Created a data platform prototype with ingestion, processing, and insights generation using a vector database using openai model (text-embedding-ada-002),python. Check out all codes here & exploring it further: GitHub Repo https://lnkd.in/gY2cE6pm #AI #MachineLearning #DeepLearning#OpenAI #Llama 3.1#Colab #DataScience

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💥💥💥 DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models Keda Tao, Can Qin, Haoxuan You, Yang Sui, Huan Wang Abstract Video large language models (VLLMs) have significantly advanced recently in processing complex video content, yet their inference efficiency remains constrained because of the high computational cost stemming from the thousands of visual tokens generated from the video inputs. We empirically observe that, unlike single image inputs, VLLMs typically attend visual tokens from different frames at different decoding iterations, making a one-shot pruning strategy prone to removing important tokens by mistake. Motivated by this, we present DyCoke, a training-free token compression method to optimize token representation and accelerate VLLMs. DyCoke incorporates a plug-and-play temporal compression module to minimize temporal redundancy by merging redundant tokens across frames, and applies dynamic KV cache reduction to prune spatially redundant tokens selectively. It ensures high-quality inference by dynamically retaining the critical tokens at each decoding step. Extensive experimental results demonstrate that DyCoke can outperform the prior SoTA counterparts, achieving 1.5X inference speedup, 1.4X memory reduction against the baseline VLLM, while still improving the performance, with no training. 👉 https://lnkd.in/dw5Bx2Aw #machinelearning

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Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐

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I studied the paper on Kolmogorov-Arnold Networks (KANs) and they are quite unique and fascinating, especially their claim to outperform the classic Multi-Layered Perceptrons (MLPs). https://lnkd.in/gFXN8fTq Some key insights: - KANs are based on the Kolmogorov-Arnold representation theory, which simply gives us a way to write a higher dimensional function in terms of a bunch of 1-D functions. - KANs offer higher accuracy than MLPs and are even more interpretable, but the main problem is their slow training. The paper mentions "KANs are typically 10x slower than MLPs, given the same number of parameters". I think the main reason is that there are so many activation functions (B-splines) in the network, which makes batch computation slower than MLPs. - KANs are quite parameter efficient. For example, the paper points out that for PDE solving, a 2-layer width-10 KAN is 100 times more accurate and 100 times more parameter-efficient than a 4-layer width-100 MLP. The paper suggests that the slow training of KANs is more of a technical challenge than a fundamental limitation, suggesting potential for optimization in the future. Even if they are super efficient and accurate, their slow training really demotivates people to look into it more. I think it's very cool and could have an interesting future. Here's a small mathematical breakdown of the model #AI #deeplearning #machinelearning #researchpaper #KAN

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Top 3 Learnings from Chip Huyen O'reilly podcast. 🌟 2 reasons limiting most of the companies from using Gen AI in Customer facing applications: Reproducibility, Compliance (GDPR,EU AI ACT etc) 🌟 2 ways most companies approach Gen AI: Problem first: We have this problem , Can we use Gen AI to solve it better . Chaos Monkey Research: Ask all your Senior to start using Gen AI foundation models, test and come up with new use cases But having Applied Research team for LLM's doesn't make it sense 🌟AI Engineering : Because we have good foundation models and spend lot less time on model development AI engineers are expected to be full stack engineers (end to end) with knowledge of front end engineering etc https://lnkd.in/ez7TEQAC

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