In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit surprising abilities across a variety of language tasks. |
| Approach: | They propose an algorithm which selects a coreset by analyzing correlation between training and evaluation samples with a trained model. |
| Outcome: | The proposed algorithm can achieve similar performance with just 50% of the training data while preserving the accuracy of the existing model. |
Similar Papers
JI2S: Joint Influence‐Aware Instruction Data Selection for Efficient Fine‐Tuning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior selection strategies score samples using generalpurpose LLMs, leveraging their strong language understanding but introducing inherent biases that misalign with the target model’s behavior and yield unstable downstream performance. |
| Approach: | They propose a framework that jointly models marginal and combinatorial influences within sample groups and evaluate them on Open LLM Benchmarks, MTBench, and GPT4–judged pairwise comparisons. |
| Outcome: | The proposed framework outperforms fulldataset training and strong baselines on Open LLM Benchmarks, MTBench, and GPT4–judged pairwise comparisons. |
Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Influence-based methods show promise in achieving (1), but often struggle with (2) . data selection is often biased towards high-influence tasks, harming performance on them . |
| Approach: | They propose a Balanced and Influential Data Selection algorithm that normalizes influence scores of training data and iteratively chooses the training example with the highest influence on the most underrepresented task. |
| Outcome: | The proposed model outperforms both state-of-the-art influence-based methods and non-influence-based frameworks on seven benchmarks spanning five diverse capabilities. |
DEFT-UCS: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection for Text-Editing (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in language modelling have led to the availability of many pre-trained language models (PLMs); however, how much data is needed to fine-tune PLMs for downstream tasks? |
| Approach: | They propose a data-efficient fine-tuning framework that leverages unsupervised core-set selection to identify a smaller, representative dataset to fine- tune PLMs for text-editing tasks. |
| Outcome: | The proposed framework is as accurate as CoEDIT across eight different datasets consisting of six different editing tasks, while finetuning on 70% less data. |
Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies focus on data selection but lack a clear, unified framework . variability in experimental settings complicates systematic comparisons . |
| Approach: | They propose a three-stage scheme to standardize data selection for fine-tuning large language models . they propose unified comparison approach that incorporates ratio-based efficiency and ranking-based feasibility metrics to address inconsistencies across experiments. |
| Outcome: | The proposed scheme outperforms existing methods in a dozen key studies and identifies key challenges. |
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. |
Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance (2025.emnlp-main)
Copied to clipboard
| Challenge: | Extensive experiments demonstrate that our approach significantly alleviates task interference and forgetting. |
| Approach: | They propose a framework for supervised fine-tuning for large language models . they first fine-tail the model on each task to identify its core parameter regions . |
| Outcome: | The proposed framework outperforms vanilla fine-tuning and baselines on multiple public benchmarks on reasoning, dialogue, instruction following, and more. |
OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs (2024.naacl-demo)
Copied to clipboard
| Challenge: | Current research seeks to de-bias such models, or suppress potentially biased answers. |
| Approach: | They present a web demo to test the biases of instruction-tuned Large Language Models . they identify 11 different biase based on a corpus of data . |
| Outcome: | The proposed demo shows that biases in instruction-tuning are explicit and transparent . the demo shows how the model was trained and showcases the web application . |
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction tuning language models can be expensive and expensive to train . current methods require extensive training on large datasets, resulting in high training costs. |
| Approach: | They propose a novel approach to selecting training data based on the learning percentage of the samples. |
| Outcome: | The proposed model performs better on models ranging from 1B to 13B in size compared to training on the entire dataset. |
Do Influence Functions Work on Large Language Models? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Influence functions are important for quantifying the impact of individual training data points on a model’s predictions. |
| Approach: | They conduct a systematic study to address a key question: do influence functions work on large language models? |
| Outcome: | The influence functions perform poorly across multiple tasks and are therefore unsuitable for large language models. |
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. |