Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection (2025)

Abstract: Evol-Instruct has made significant improvements as a data synthesis method in several areas. Existing methods typically rely on a fixed set of strategies to evolve, which require manual design and are monolithic in form. In addition, iterative evolution also makes the acquisition of hard samples expensive. In view of this, we propose the Tag-Evol framework, a more diverse and efficient instruction evolving method. Specifically, Tag-Evol uses diverse and specific knowledge tags as strategies to achieve controlled evolution by injecting different combinations of tags into the original instructions. Experiments with multiple backbones in mathematical and code domain benchmarks show that the proposed method generates significantly better evolved data than other methods. Furthermore, we conduct a thorough analysis of the evolved data, demonstrating that Tag-Evol is not only efficient but also generates more diverse and challenging data.

## introduction as a key stage in the training of large language models (llms), supervised fine-tuning (sft) greatly improves the performance of the models on various natural language processing tasks @xcite . to achieve better alignment results in the sft phase, many high-quality instruction datasets (e.g., sharegpt @xcite , lima @xcite ) have been collected and proposed. however, human-annotated high-quality datasets are usually expensive and limited, which cannot support larger scale sft of the model. in light of this, recent researches @xcite @xcite has focused on how to synthesize instruction data for training using llms themselves, which is relatively cheap and scalable. evol-instruct @xcite ., † equal contribution. * corresponding author. while evol-instruct has shown excellent results on a variety of tasks and models, there are still some issues that need to be addressed: (1) fixed evolution strategies. existing methods obtain a fixed set of evolution strategies through heuristic human annotation and machine optimization, which is costly and hardly adaptable to other domains. simultaneously, fixed strategies also limit the diversity of evolved data due to preference bias of the model itself. (2) inefficient synthesis mode. in order to obtain the hard version of instructions, evol-instruct requires multiple iterations of evolution, which not only take extra time, but also introduce cumulative errors due to hallucinations. an ideal instruction evolving algorithm should have diverse and specific evolution strategies and be able to efficiently generate synthetic data of varying difficulty. to address the above challenges, we propose tag-evol framework, a more efficient approach for evolved instruction data synthesis through knowledge tag information injection. inspired by @xcite , who evaluates the difficulty of instructions based on the number of knowledge tags typed by llms, we redesign the instruction evolving method from the perspective of knowledge tags. these tags represent the attributes contained in the instruction. as shown in figure 1 , we use diverse and specific knowledge tags as enriched evolution strategies, and use different combinations and numbers of knowledge tags to explicitly control the degree of evolution. in this way we can avoid the fixed strategy by combining tags and control the number of injected tags to directly generate hard samples without iteration. specifically, our tag-evol framework can be divided into two parts, tag pool construction and tag sampling evolution. firstly, we obtain a domainspecific tag pool by tagging and merging the samples in the seed dataset. unlike tagging methods used for evaluation, we want to acquire tags that are as diverse and specific as possible, and for this reason we propose a multi-step fine-grained knowledge tagging approach (see details in §3.1) that can significantly improve the quantity and quality of the tag pool. after obtaining the tag pool, we guide the synthesis of the instruction data by sampling different numbers of knowledge tags as the evolution strategy (see details in §3.2). by prompting the llms to inject the knowledge of different tag combinations into the seed dataset, we are able to achieve more accurate instruction evolution. we have conducted experiments using several mainstream llms as backbones on multiple tasks including multi-turn conversation, instruction following, mathematical reasoning, and code generation. for a fair comparison, we replicat the evol-instrut @xcite and auto evol-instruct @xcite as our baseline methods un-der the same open source setup. the results show that the proposed tag-evol significantly outperforms the baseline model under different backbones, achieving better downstream task performance on multiple domains. in addition, we have also conducted exhaustive analytical experiments on the proposed method in terms of the size of the required evolution model, the number of iterative convergence, demonstrating the great potential of the tag-evol method in the field of synthetic data. our main contributions can be summarised as follows: while most synthetic data methods are implemented using closed-source models (e.g., chatgpt @xcite ), there has been some recent works focus on how to use open-source models for tag candidates ... budget budget 2 3 instruction: a square has a side length that is 64% of a certain number. if the area of the square is 256 square units, what is the original number? instruction: write a java program that reverses a given string while excluding vowels using regular expressions. ensure the final reversed string does not contain consecutive duplicate characters. 3. sample selected tags: ['exponentiation','geometry'] selected tags: ['duplicate','regex','java'] stage1 tag pool construction ( §3.1) stage2 tag sampling evolution ( §3.2) create a c++ program for randomly shuffling an array of integers. rationalize the denominator: data synthesis. xu et al. (2024) exploit the design of open-source model templates and utilize special tokens to distill high quality instruction data from the sft phase of the model. ding et al. (2024) further propose the question fine-tuning (qft) task, which takes instruction data synthesis as a task for the model to learn. it enables the fine-tuned qft model to extract more high-quality instruction data. hui et al. (2024) compare the performance of different scale models in the evol-instruct method and demonstrate that small models are more suitable for generating diverse evolved data. evaluation with llms. in addition to the generation of synthetic data, how to automatically evaluate and filter synthetic data using llms is also an important issue. @xcite propose the mt-bench dataset to evaluate the performance of llm-based chat assistants by scoring them with a powerful llm judge. @xcite effectively evaluate and filter the sft phase training data in terms of three dimensions: complexity, quality, and diversity. unlike directly scoring, @xcite propose the instag method to assess the difficulty and diversity of instruction data from the perspective of knowledge tags. by tagging each sample using llms, instag can estimate the difficulty of different samples by the number of tags and the diversity of the dataset by the total number of tags. instag proposes a systematic approach to data evaluation, but existing synthetic data approaches do not take full advantage of the benefits of tags in knowledge representation. in this paper, we propose the tag-evol framework for efficient and high-quality instruction evolution using the content and number of tags as evolution strategies. ## tag-evol framework as shown in figure 2 , our tag-evol framework can be divided into two parts as a whole: tag pool construction ( §3.1) and tag sampling evolution ( §3.2). in the tag pool construction phase, our goal is to extract tags containing extensive knowledge from the seed dataset as strategies for subsequent evolution. after obtaining the tags, we implement the evolution of instructions with various levels of difficulty by randomly sampling a certain number of tags and injecting them into the original dataset utilizing the rewriting ability of llms.