Challenge: Llama-Krikri-8B is a cutting-edge Large Language Model for the Greek language based on Meta's Llma 3.1-8B.
Approach: They propose to use Llama-Krikri-8B to train Greek language models . it has 8 billion parameters and is capable of handling polytonic text and Ancient Greek .
Outcome: The proposed model is based on Meta's Llama 3.1-8B and has 8 billion parameters and is capable of handling polytonic text and Ancient Greek.

Similar Papers

GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek (2026.findings-acl)

Copied to clipboard

Challenge: Existing evaluation benchmarks for large language models are limited for Greek . Existing datasets are often machine-translated from English, failing to capture Greek linguistic and cultural characteristics.
Approach: They propose a native-sourced benchmark for massive multitask language understanding in Greek . they publicize 16,857 samples and reserve 4,948 samples for a private leaderboard .
Outcome: The proposed model is based on 21,805 multiple-choice questions across 45 subject areas . the model is publicly released and reserved for a private leaderboard .
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024.acl-demos)

Copied to clipboard

Challenge: Efficient fine-tuning of large language models requires non-trivial efforts to implement these methods on different models.
Approach: They propose a framework that democratizes the fine-tuning of large language models by integrating a suite of efficient training methods into one framework.
Outcome: The proposed framework is able to scale to 100+ LLMs without coding and receives over 25,000 stars and 3,000 forks.
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)

Copied to clipboard

Challenge: Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal.
Approach: They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages.
Outcome: The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark.
LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback (2024.findings-acl)

Copied to clipboard

Challenge: Recent multilingual models support limited number of human languages due to lack of training data for low resource languages.
Approach: They propose a multilingual multilingual LLM that scales to 100 languages . they use a human feedback dataset and a data set to perform multilingual instruction tuning .
Outcome: The proposed model outperforms its peers on five multilingual benchmarks.
Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025.coling-main)

Copied to clipboard

Challenge: Popularity of Large Language Models (LLMs) has seen a skyrocketing increase in recent years.
Approach: They present a systematic review of the language adaptation process for Large Language Models including vocabulary expansion, continued pre-training, and instruction fine-tuning.
Outcome: The proposed model is based on empirical studies conducted on LLaMA2 and discussions on various settings affecting the model's capabilities.
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)

Copied to clipboard

Challenge: Large language models exhibit significant performance discrepancies between high- and low-resource languages.
Approach: They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset.
Outcome: The proposed model achieves consistent multilingual representations across languages.
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Copied to clipboard

Challenge: Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach: They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome: The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.
MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline (2024.findings-acl)

Copied to clipboard

Challenge: Large language models lack mathematical reasoning, a hurdle on the path to true artificial general intelligence.
Approach: They propose a protocol for fine-tuning large language models with a Python code interpreter to enhance the text analysis of the LLMs.
Outcome: The proposed protocol improves the performance of a 7B-parameter LLM on the GSM8K and MATH datasets while allowing for an outlier-free value model-based inference method.
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

Copied to clipboard

Challenge: a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach: They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome: The proposed library is based on extensive experiments in a variety of evaluation settings.
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models (2026.findings-acl)

Copied to clipboard

Challenge: Long-context capabilities are essential for document and video understanding, in-contact learning, and inference-time scaling.
Approach: They propose an efficient training recipe for building ultra-long context LLMs from aligned instruct model, pushing the boundaries of context lengths from 128K to 1M, 2M, and 4M tokens.
Outcome: The proposed model extends the context window while maintaining short context capabilities while maintaining the performance of the existing model.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations