GPT-SW3: An Autoregressive Language Model for the Scandinavian Languages (2024.lrec-main)
Copied to clipboard
Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk, Joey Öhman, Tim Isbister, Evangelia Gogoulou, Fredrik Carlsson, Judit Casademont, Magnus Sahlgren
| Challenge: | a growing interest in building and applying large language models for languages other than English is fueling interest in developing LLMs for smaller languages. |
| Approach: | They describe the development process for the first native large generative language model for the North Germanic languages, GPT-SW3. |
| Outcome: | The proposed model is based on the generative language model for the North Germanic languages . it is a first-generation model with a high-quality data set and a low cost of implementation . |
Similar Papers
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish (2022.lrec-1)
Copied to clipboard
Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren
| Challenge: | a prerequisite for building large-scale generative models for other languages is access to large amounts of high-quality text data and powerful computational resources. |
| Approach: | They present a 3.5 billion parameter autoregressive language model, trained on a 100 GB Swedish corpus. |
| Outcome: | The proposed model performs well on a 100 GB Swedish corpus and is competent in comparison with existing models of similar size. |
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
Copied to clipboard
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation. |
| Approach: | They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field. |
| Outcome: | The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models. |
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results. |
| Approach: | They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
| Outcome: | The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
Data and Model Centric Approaches for Expansion of Large Language Models to New languages (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
| Approach: | This tutorial examines approaches to expand the language coverage of LLMs . they look at tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc. |
| Outcome: | This tutorial examines approaches to expand the language coverage of LLMs . it provides an efficient and viable path to bring LLM technologies to low-resource languages . |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
FinGPT: Large Generative Models for a Small Language (2023.emnlp-main)
Copied to clipboard
Risto Luukkonen, Ville Komulainen, Jouni Luoma, Anni Eskelinen, Jenna Kanerva, Hanna-Mari Kupari, Filip Ginter, Veronika Laippala, Niklas Muennighoff, Aleksandra Piktus, Thomas Wang, Nouamane Tazi, Teven Scao, Thomas Wolf, Osma Suominen, Samuli Sairanen, Mikko Merioksa, Jyrki Heinonen, Aija Vahtola, Samuel Antao, Sampo Pyysalo
| Challenge: | Neural language models excel in many tasks in NLP but are limited to smaller languages. |
| Approach: | They propose two approaches to pretrain large language models for Finnish . they train seven monolingual models from scratch and use Finnish as pretraining data . |
| Outcome: | The proposed model is based on a dataset of Finnish web crawls, news, social media and eBooks. |
As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing pre-trained language models are limited in their ability to train for English, which is a problem for many other languages. |
| Approach: | They propose to adapt existing generative language models to new languages by retraining lexical embeddings without tuning the Transformer layers. |
| Outcome: | The proposed method achieves lexical embeddings for Italian and Dutch that are aligned with the original English lexicals. |
Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)
Copied to clipboard
Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu
| Challenge: | Existing surveys focus on LLMs' specific utility for data annotation and synthesis. |
| Approach: | They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations . |
| Outcome: | The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information. |
Mapping 1,000+ Language Models via the Log-Likelihood Vector (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to compare autoregressive language models are based on log-likelihoods . a model map is constructed using coordinates that capture the geometric structure of probability distributions based upon text-generation probabilities. |
| Approach: | They propose to use log-likelihood vectors to compare autoregressive language models . when treated as model features, their squared Euclidean distance approximates KL divergence . |
| Outcome: | The proposed method is highly scalable and easy to implement. |