Empirical Analysis of Task Mixture Effects in Small-scale Instruction Tuning: A Statistical Approach (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that instruction tuning can significantly vary depending on the task and format diversity of training data. |
| Approach: | They conduct experiments on unlabeled instruction corpora to identify effective mixtures. |
| Outcome: | The proposed model can achieve up to 5.7 speedup in training with 1,000 curated examples. |
Similar Papers
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
Copied to clipboard
| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? (2024.emnlp-main)
Copied to clipboard
Alexander Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
| Challenge: | a study of multilingual pre-trained LLMs on parallel instruction-tuning benchmarks shows that instruction-following models can be used across languages by up to 9.9%. |
| Approach: | They conduct an extensive study of the performance of multilingual pre-trained LLMs instruction-tuned on parallel instruction-uning datasets. |
| Outcome: | The proposed model improves cross-lingual instruction following capabilities by 9.9% on a large and mid-sized LLM on parallel instruction-tuning datasets. |
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM (2025.coling-main)
Copied to clipboard
| Challenge: | Many large language models (LLMs) support many languages, while others only support a few, e.g. the Llama series. |
| Approach: | They present a case study on BLOOM to understand three pertinent factors affecting performance: the number of languages, language exposure, and similarity between training and test languages. |
| Outcome: | The proposed model can be used to perform multilingual tasks on 1 to 52 languages. |
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models have shown impressive performance in following natural language instructions to solve unseen tasks. |
| Approach: | They propose two strategies to help large language models better leverage task instructions . they propose to remove 60% of tokens from the task definitions while maintaining model performance . |
| Outcome: | The proposed approach achieves 4.2 Rouge-L improvement over 119 unseen test tasks. |
Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for data mixture improve the generalization capability of large language models (LLMs) on downstream tasks. |
| Approach: | They propose a fine-grained categorization of existing methods and propose three subtypes of offline and online methods. |
| Outcome: | The proposed methods extend beyond offline and online classifications and highlight key challenges in the field of data mixture. |
Chasing Random: Instruction Selection Strategies Fail to Generalize (2025.findings-naacl)
Copied to clipboard
| Challenge: | Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. |
| Approach: | They analyze popular selection strategies across different datasets and benchmarks to find out whether they generalize poorly. |
| Outcome: | The proposed methods outperform random baselines and cost-performance trade-offs on the full dataset and a random subset. |
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained large language models retain task-specific knowledge, but where and to what extent they retain it remains unexplored. |
| Approach: | They investigate the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations across over 60 NLP tasks. |
| Outcome: | The results show that pre-trained models retain task-specific knowledge . some tasks are already encoded in pre-train models, but others benefit from instruction tuning. |
Fine-Tuning Large Language Models with Sequential Instructions (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing instruction-tuned models struggle to adhere to a query with multiple intentions, which impairs their performance when the completion of several tasks is demanded by a single command. |
| Approach: | They develop an automatic process that turns existing data into diverse and complex task chains and a new benchmark to evaluate a model’s ability to follow all the instructions in a sequence. |
| Outcome: | The proposed model can follow instructions better and deliver higher results in coding, maths, and open-ended generation. |
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)
Copied to clipboard
| Challenge: | Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning. |
| Approach: | They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes. |
| Outcome: | The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget. |