A rg I nstruct: Specialized Instruction Fine-Tuning for Computational Argumentation (2025)

Abstract: Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilities, instruction-following LLMs encounter difficulties when dealing with tasks that require domain knowledge. This work introduces a specialized instruction fine-tuning for the domain of computational argumentation (CA). The goal is to enable an LLM to effectively tackle any unseen CA tasks while preserving its generalization capabilities. Reviewing existing CA research, we crafted natural language instructions for 105 CA tasks to this end. On this basis, we developed a CA-specific benchmark for LLMs that allows for a comprehensive evaluation of LLMs’ capabilities in solving various CA tasks. We synthesized 52k CA-related instructions, adapting the self-instruct process to train a CA-specialized instruction-following LLM. Our experiments suggest that CA-specialized instruction fine-tuning significantly enhances the LLM on both seen and unseen CA tasks. At the same time, performance on the general NLP tasks of the SuperNI benchmark remains stable.

## introduction large language models (llms) have proven effective for various nlp tasks, including several tasks from computational argumentation (ca), the computational analysis and synthesis of natural language arguments @xcite . initially, it was common to fine-tune pretrained llms on input-output pairs for a task @xcite . figure 1 (a) illustrates such task-specific fine-tuning for the mining of claims and premises from student essays. in contrast, recent llms are often instruction fine-tuned by exposing them to highly diverse tasks 1 (ouyang et al., ## methodology this section presents our methodology for the training of a specialized llm for computational argumentation (ca). we start by deriving a seed set of instruction fine-tuning tasks from existing ca tasks and datasets. this manually-annotated seed data serves as a reliable basis for generating a large set of diverse ca-specific tasks. combining these tasks with general nlp tasks, we then specialize an llm using instruction fine-tuning, dubbed arginstruct (argumentation-specialized instruction fine-tuning). whereas standard instruction fine-tuning is, by concept, fully generalization-oriented, we diversify instructions only within the task domain. the ca-specific instructions enable the llm to deal specifically with any task from computational argumentation. by still mixing in general instruction fine-tuning data, we further achieve that generalization capabilities are widely preserved. figure 2 illustrates the methodology. ## data this section first details the selection process for ca seed datasets and tasks that serve as the basis for arginstruct. then, we present the resulting ca-specific instruction fine-tuning dataset, which integrates the seed tasks and newly generated tasks. lauscher et al. (2022) (30), as well as more recent datasets found through searches in the acl anthology and google scholar (35). we then categorized each dataset into one or more ca areas based on their usage in the literature: argument mining, argument assessment, and argument generation. 3 for each subarea, we selected a subset of ten datasets, consisting of the five most cited datasets along with five additional, lesser-known datasets that cover a diverse range of data sources. this process ensures comprehensive coverage of ca and results in the 30 seed datasets listed in ca seed task collection given the seed data, we obtained the set of ca seed tasks, t 0 , from the respective papers. following our task definition, we consider a task @xmath0, i) to model a relation s between inputs and outputs, which can be described in natural language in the form of an instruction i. intermediate steps (e.g., feature extraction or data preprocessing) are not seen as individual ca tasks. if papers contained multiple tasks (e.g., mining and relation identification), each task was treated separately. we wrote the corresponding instructions based on annotation guidelines or, when unavailable, the task descriptions in the papers. to ensure tasks are self-contained, we included relevant term definitions (e.g., class definitions) in the instructions (examples in appendix b). in total, we obtained a set of 105 seed tasks, t 0 = {t 1 , . . . , t 105 }, from the 30 seed datasets. we categorized all see tasks into three task types: classification, regression, and generation. there is a noteworthy connection between task types and ca subareas: argument mining tasks are typically categorized as classification, argument assessment tasks as regression, and argument generation tasks as generation. however, exceptions exist; for instance, argument mining can be modeled as a classification task at the sentence level or as a generation task at the span level. ca task generation based on the 105 seed tasks, we generated 52,445 additional ca tasks. by roughly matching the size of the general dataset of @xcite , we ensure comparability to their results. concretely, we used meta-llama-3-70b as the llm to generate new ca instructions (step 1 in figure 2 ), filter them by ca relevance (step 2), and generate instances (step 3). ## evaluation we now present our experiments to evaluate the impact of specialized instruction fine-tuning. llm variants were trained on (a) ca seed tasks, (b) generated ca tasks, (c) general tasks, and (d) combinations thereof. we then assessed their performance on unseen instances of known ca tasks and their generalizability to entirely unseen ca tasks. ## conclusion despite their strong generalization capabilities, instruction-following llms struggle with tasks that require domain knowledge. we propose arg-instruct, a new specialized instruction fine-tuning method to address this issue for the domain of computational argumentation (ca). as a starting point, we have collected 105 ca tasks from the literature and crafted natural instructions for each that serve as a benchmark for llmbased ca. additionally, we have generated 52k ca-specific tasks, adapting the self-instruct process to bridge between generalization and ca specialization. we have then trained ca-specialized instruction-following llms, combining the collected and generated ca tasks with general instruction fine-tuning data. our experiments suggest that an llm fine-tuned on the combined data performs best on unseen ca tasks without losing its general instruction-following capabilities. while the llm did not fully reach single-task sota results, it is on par in half the tasks. at the same time, it outperforms several existing instruction-following models, including the proprietary gpt-4o-mini. we conclude that our arginstruct method denotes a substantial step towards overcoming the domain challenges of llms, providing a benchmark dataset and a task-agnostic model for ca. we expect that our method may be well-transferable to other specialized nlp domains, for example, to the educational domain. there, our method could involve collecting seed tasks such as essay scoring, feedback generation, text suggestion, and rewriting, but we leave this to future work.