MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Prior work on multilingual evaluation has shown that there is a large gap between the performance of Large Language Models on English and other languages. |
| Approach: | They propose to finetune Llama-2 and Mistral models on two datasets to determine their effect on model performance on six downstream tasks covering forty one languages. |
| Outcome: | The proposed model can improve on six multilingual tasks while degrading on high-resource languages. |
Similar Papers
VEEF-Multi-LLM: Effective Vocabulary Expansion and Parameter Efficient Finetuning Towards Multilingual Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a significant disadvantage for low-resource languages . VEEF-Multi-LLM-8B excels in multilingual instruction-following tasks . |
| Approach: | They propose a low-resource multilingual large language model that expands the vocabulary for multilingual support. |
| Outcome: | The proposed model outperforms existing models in multilingual instruction-following tasks, but lags behind English-centric models in some tasks. |
Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models that target a single language are not seen during finetuning, but are able to respond in multiple languages once deployed in downstream applications. |
| Approach: | They investigate the minimal amount of multilinguality required during finetuning to elicit effective cross-lingual generalisation in English-centric LLMs. |
| Outcome: | The proposed model can respond in as few as two to three languages to a user's query in English, but the degree to which a target language is seen during pretraining is limiting. |
Finetuning LLMs for Comparative Assessment Tasks (2025.coling-main)
Copied to clipboard
| Challenge: | Automated assessment in natural language generation is a challenging task. |
| Approach: | They propose a framework for fine-tuning LLMs for comparative assessment to align the model’s output with the target distribution of comparative probabilities. |
| Outcome: | The proposed framework improves state-of-the-art performance while maintaining high performance with an efficient subset of comparisons. |
A Semantic-Aware Layer-Freezing Approach to Computation-Efficient Fine-Tuning of Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work on how to finetune but neglects the issue of where to fine-tune language models is expensive. |
| Approach: | They propose to use transition traces of latent representation to compute deviations (or loss) and then estimate the gain of each layer in reducing deviation (or gain). |
| Outcome: | The proposed approach outperforms baseline methods and is cost-benefit balanced. |
Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
Copied to clipboard
| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
| Approach: | They propose to review parameter-efficient fine-tuning techniques that lower training and deployment costs and domain and cross-lingual adaptation methods for both encoder and decoder models. |
| Outcome: | The proposed techniques lower training and deployment costs, domain and cross-lingual adaptation methods, and model specialization strategies. |
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing open-source LLMs exhibit limited effectiveness in processing Vietnamese . lack of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation exacerbates these issues. |
| Approach: | They propose to fine tune LLMs specifically for Vietnamese and develop a framework for evaluation . they find that larger models introduce more biases and uncalibrated outputs . |
| Outcome: | The proposed framework finetunes LLMs specifically for Vietnamese and provides a framework for evaluation . |
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation (2025.findings-acl)
Copied to clipboard
| Challenge: | Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages. |
| Approach: | They propose to fine tune generative large language models to provide safe responses to harmful user input and to use direct preference optimization to mitigate toxicity. |
| Outcome: | The proposed models show that finetuning on specialized datasets reduces biases but also produces fluent and diverse text in non-English languages. |
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods such as LoRA and VeRA use a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Approach: | They propose a parameter-efficient fine-tuning approach that leverages a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Outcome: | The proposed approach outperforms existing methods on GLUE and E2E benchmarks and is effective in instruction-tuning large language models and image classification models. |
How Vocabulary Sharing Facilitates Multilingualism in LLaMA? (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) show strong performance on English tasks, but their performance in other languages is limited. |
| Approach: | They conducted an exhaustive analysis of the multilingual capability of LLMs by examining the performance gap before and after embedding fine-tuning across 101 languages. |
| Outcome: | The proposed model improves on the attributes of four quadrants in the model and provides actionable and efficient guidelines for tuning these languages. |
Low-Rank Adaptation for Multilingual Summarization: An Empirical Study (2024.findings-naacl)
Copied to clipboard
| Challenge: | Pre-trained Large Language Models have significantly advanced NLP, but their ever-increasing size poses significant challenges for conventional fine-tuning. |
| Approach: | They investigate the potential of Low-Rank Adaptation (LoRA) in multilingual summarization, a task that is challenging and relatively unexplored. |
| Outcome: | The proposed method outperforms full fine-tuning and cross-lingual transfer strategies in multilingual summarization tasks. |