LLM DEBATE OPPONENT : Counter-argument Generation focusing on Implicit and Critical Premises (2025)

Abstract: Debate education fosters critical thinking skills but often incurs high human costs. Recent advancements in Large Language Models (LLMs) show promise in automating counter-argument generation. However, it remains unclear how best to guide LLMs to target both implicit and critical premises. In this study, we systematically compare multi-step and one-step generation methods for counter-arguments across 100 debate topics. Our findings reveal that one-step approaches consistently outperform multi-step pipelines, owing to their better grasp of the “motion spirit,” minimized propagation of hallucinations, and avoidance of challenging intermediate tasks. Among premise-targeting methods, a one-step strategy that accounts for both implicit and explicit premises—Generated and Targeted Premise Attack (GTG)—emerges as the strongest performer in expert and automated evaluations. These results highlight the value of direct, integrated prompts for leveraging LLMs in complex argumentation tasks and offer insights for developing more effective automated debate agents.

## introduction in our highly information-oriented society, the development of critical thinking skills 1 is a national priority. it is said that these skills are fostered through debate education. however, debating requires a human cost, such as an opponent and an evaluator. we are therefore developing a debate opponent using large language 1 logical, objective, and unbiased reasoning, characterized by reflective thinking that involves the conscious examination of one's own reasoning processes @xcite . models (llm) agents with powerful natural language processing capabilities. it is expected that learners will experience various types of arguments, represented by weakening arguments by denying premises @xcite , through this debate against the debate opponents. this exposure to diverse argumentative strategies is expected to enhance the learners' capacity for critical thinking skills @xcite . in developing llms as debate agents, a critical consideration is their ability to generate counterarguments. even in this era of rapidly advancing llms, which have seen big progress in text generation capabilities @xcite @xcite , research focused on the generation of counterarguments continues to attract considerable interest within the field. however, the feedback from debate experts foot_0 suggests that counter-arguments generated by llms often lack argumentative strength. in fact, the gpt-3.5-turbo based debate opponent we developed in our preliminary experiments has been defeated by middle school students who have learned to debate in english competitive debate competitions (as of march 2023). in competitive debate, the strength of the counterargument hinges on the validity of the premise being attacked. for instance, when countering "homework should be abolished because it infringes on free time," challenging the implicit and critical premise that "free time is inherently more productive" proves more strong @xcite . therefore, it is important how implicit and critical the premise can be attacked. several studies focusing on premises in debate exist. @xcite proposed a two-step framework using bert and gpt-2 to directly target and refute key premises, outperforming earlier lstm-based methods in generating counter-arguments. however, the above proposed method limits the premise to be attacked to explicit ones and does not clarify the criteria or definition of critical premises. in this study, we proposed a method (see figure 1 ) to make llm imitate the thought process that debate experts implicitly follow when constructing a counter-argument: first, they organize premises that support the affirmative argument, then they decide which premises to attack, and then they create a counter-argument. in this study, we collaborated with debate experts to independently design a definition of critical premises and proposed a method (see figure 1 ) that enables llms to mimic the implicit reasoning processes that debate experts naturally employ when constructing counter-arguments. by doing so, we aim to incorporate implicit premises as potential targets for attack, thereby generating counterarguments with greater argumentative strength. our approach consists of three key steps. in the first step, the llm receives a debate topic along with its corresponding affirmative claim and generates a comprehensive list of premises that support the claim, regardless of whether they are implicit or explicit. in the second step, the model identifies which premises to attack based on the predefined criteria for critical premises. finally, in the third step, the llm constructs counter-arguments that specifically target the selected premise. we evaluated our approach from two key perspectives: (1) whether the target premises for attack should include implicit premises (i.e., whether step 1 should be performed), and (2) whether pro-viding predefined critical premises impacts performance. as a baseline, we used a simple direct counter-argument generation approach. furthermore, considering prior research indicating that llm performance improves when reasoning processes are explicit, as seen in chain-of-thought (cot) prompting @xcite , we investigated whether our method's performance differs when all steps are instructed at one-step versus when each step is executed separately in a multi-step prompting. therefore, the purpose of this paper is to evaluate and compare both the multi-step and one-step approaches to counter-argument generation from two perspectives: whether implicit premises are also added to the candidate attack premises or whether the definition of critical premises is used. the contributions of this study are presented below. through comprehensive evaluation involving human experts and automated assessment, we investigate these approaches' effectiveness in generating strong counter-arguments, aiming to contribute to the development of more effective debate agents. however, our research focuses specifically on the identification and targeting of implicit and critical premises in counter-argument generation, rather than on the procedural approach itself (multi-step or one-step). we suggest that effective counterarguments should target premises that are critical to the basis of the argument but often left implicit by the arguer. thus, our key suggestion is to focus on the quality of the premises rather than the generative process. showing that llm responses can match or surpass human outputs in logical coherence. more recent work has leveraged multi-agent interactions among llms with distinct personas @xcite and self-refinement techniques @xcite @xcite to further enhance diversity and depth. premise-focused methods. attacking premises is a core strategy in debate @xcite . @xcite proposed a bert-gpt-2 pipeline for identifying and refuting key premises, outperforming lstm-based methods. accounting for implicit premises can reveal hidden assumptions, as demonstrated by @xcite . ## methods we categorize our counter-argument generation approaches into multi-step and one-step methods, each reflecting a distinct strategy for producing counter-arguments. the multi-step approach imitates the systematic analytical process of human experts, splitting the generation into phases that can enhance transparency and explainability. by contrast, the one-step approach merges these phases into a single step, while still aligning with the expert-inspired pipeline. as a baseline, we consider a direct counter-argument generation method that does not attempt to replicate expert reasoning. ## construction of dataset we collected debate topics and affirmative arguments from idebate foot_1 , a well-known debate forum. we randomly selected 100 instances from the scraped data and used an llm (clade-3.5-sonnet) to refine them into clear, concise sentences while maintaining the original content. examples are shown in we conducted a comparative evaluation experiment of four counter-argument generation methods. using a 100-set dataset, we generated counterarguments using three llms: gpt-4o-mini-2024-07-18 (mini") and gpt-4o-2024-05-13 (gpt") from openai foot_2 , and llama-3.1-70b-versatile ("llama") from meta foot_3 . we performed automatic evaluation using gpt-4o as evaluator, comparing methods within two groups (multi-step format + baseline and one-step + baseline) using eight evaluation metrics. the metrics were categorized as either choice or ranking type (refer to ## results and analysis the results for ranking-type evaluation metrics are shown in tables 4, 5 7 . in multi-step methods, directgen achieved highest ranks across q1-q3 metrics, followed by m-comp, m-targ, m-basic. in one-step methods, o-comp ranked highest, directgen and o-targ showed equal rates, o-basic lowest. one-step methods demonstrated superior performance except q3 comparison between basic variants. one-step methods outperform multi-step methods across all metrics. three key factors contribute to these results. first, better motion spirit capture, as llms learn affirmative claims, and counterarguments in proximity within embedding space, while decomposed steps may miss critical premises. second, reduced hallucination impact, as multi-step processes propagate hallucinations forward ( @xcite , @xcite , @xcite )), while one-step generation minimizes impact. third, premise decision difficulty is a significant challenge. selecting critical premises has been shown to be difficult even for state-of-the-art llms, with @xcite demonstrating that even powerful models 7 values averaged across three models. choice-type metrics in ## conclusion this study conducted a comprehensive comparison of different approaches to counter-argument generation using large language models, addressing the challenge of high human costs in debate education while maintaining educational effectiveness. through evaluation of seven distinct methods across 100 debate topics, we demonstrate that focusing on critical and implicit premises significantly enhances llms' ability to generate strong counter-arguments. our analysis reveals that one-step methods consistently outperformed multi-step approaches across all evaluation metrics. this superior performance can be primarily attributed to their better capture of motion spirit through llm's learned associations between topics and counterarguments. additionally, one-step methods minimize the impact of hallucinations that typically cascade through multi-step processes, while avoiding the challenging task of intermediate premise selection 5 that often proves difficult even for experienced debaters. among the methods targeting premises, o-comp achieved the highest performance in both human and automated evaluations. its success stems from the effective consideration of both explicit and implicit premises, combined with clear guidance about critical criteria within a single-step framework. the method's ability to identify and attack core assumptions proved crucial for generating compelling counter-arguments, demonstrating the importance of comprehensive premise analysis in automated argumentation. these findings contribute significantly to our understanding of how to effectively leverage llms in complex argumentation tasks and provide practical insights for developing more effective debate agents. our results suggest that while decomposed reasoning can be beneficial in many contexts, counter-argument generation benefits more from integrated approaches that allow llms to fully utilize their learned understanding of argumentative patterns. these insights pave the way for more accessible and effective debate education systems that can help address the critical need for developing students' critical thinking skills.