IT2ACL Learning Easy-to-Hard Instructions via 2-Phase Automated Curriculum Learning for Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies have focused on the pre-training phase of large language models, but this study focuses on the learning phase of pre-trained LLMs. |
| Approach: | They propose a 2-phase automated curriculum learning guided instruction tuning framework that learns easy-to-hard instructions in a self-adjusting dynamic manner. |
| Outcome: | The proposed framework unlocks latent ability in pre-trained large language models and achieving superior performance across diverse tasks. |
Similar Papers
Exploring Two-Phase Continual Instruction Fine-tuning for Multilingual Adaptation in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | A key challenge for Large Language Models (LLMs) is improving their Multilingual instruction-following ability over time without deteriorating their ability in languages they already excel at, typically English. |
| Approach: | They propose a two-phase Continual Fine-tuning setup to improve a model's Multilingual adaptability by comparing an English-only LLM with a multilingual instruction dataset. |
| Outcome: | The proposed model improves on two-phase Continual Fine-tuning (CFT) setups on a multilingual instruction dataset. |
Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets do not cover full range of chart types, such as 3D, volumetric, and gridded charts. |
| Approach: | They propose a hierarchical pipeline and a new dataset for chart generation that leverages the relationships within rich datasets. |
| Outcome: | The proposed method outperforms open-source models and is comparable to state-of-the-art proprietary models in data visualization tasks. |
Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)
Copied to clipboard
Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory. |
| Approach: | a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities . |
| Outcome: | Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory . |
Phased Instruction Fine-Tuning for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to enhance pre-trained language models' ability to follow instructions are limited due to the simultaneous handling of varying instruction complexities. |
| Approach: | They propose a phased instruction fine-tuning method that posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process. |
| Outcome: | The proposed method surpasses the one-off instruction fine-tuning method in win rate and validates the hypothesis of progressive alignment. |
Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding (2025.acl-srw)
Copied to clipboard
| Challenge: | Existing curriculum learning approaches rely on manually defined difficulty metrics which may not accurately reflect the model’s own perspective. |
| Approach: | They propose a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) they evaluate four datasets covering binary and multi-class classification tasks. |
| Outcome: | The proposed model leads to faster convergence and improved performance compared to standard random sampling. |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
Copied to clipboard
Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
Automatic Instruction Evolving for Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Evol-Instruct is an end-to-end framework that evolves instruction datasets without human effort. |
| Approach: | They propose an end-to-end framework that evolves instruction datasets without human effort by analyzing and analyzing evolutionary strategies for the given instruction data. |
| Outcome: | The proposed method outperforms human-designed methods on various benchmarks including MT-Bench, AlpacaEval, GSM8K, and HumanEval. |
From English to Second Language Mastery: Enhancing LLMs with Cross-Lingual Continued Instruction Tuning (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) acquire strong language skills through extensive pre-training and supervised fine-tuning (SFT) on instructionresponse pairs. |
| Approach: | They propose a method which leverages translation-based parallel instruction data to enhance cross-lingual adaptability. |
| Outcome: | The proposed model improves on Llama-2-7B across five languages against three objective benchmarks and an LLM-as-a-judge benchmark. |
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (2024.findings-acl)
Copied to clipboard
Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael Mahoney, Kurt Keutzer, Amir Gholami
| Challenge: | Pretrained large language models are currently state-of-the-art for solving most tasks . however, many of them are in the low-data regime, making fine-tuning challenging . a new data augmentation strategy uses a teacher LLM to augment a small seed dataset . |
| Approach: | They propose a targeted and iterative data augmentation strategy that augments a teacher LLM to fine-tune a small seed dataset by adding additional data. |
| Outcome: | The proposed approach outperforms fine-tuning and other data augmentation strategies on a small seed dataset. |
Large Language Models are Miscalibrated In-Context Learners (2025.findings-acl)
Copied to clipboard
| Challenge: | In-context Learning and Supervised Fine-Tuning have emerged as pre-dominant methodologies for machine learning and NLP. |
| Approach: | They propose to use self-ensembling to improve both performance and calibration of language models. |
| Outcome: | The proposed learning paradigms can achieve better calibration and better performance than the previous learning paradigm. |