AMERICANO : Argument Generation with Discourse-driven Decomposition and Agent Interaction (2024)

Abstract: Argument generation is a challenging task in natural language processing, which requires rigorous reasoning and proper content organization. Inspired by recent chain-of-thought prompting that breaks down a complex task into intermediate steps, we propose Americano, a novel framework with agent interaction for argument generation. Our approach decomposes the generation process into sequential actions grounded on argumentation theory, which first executes actions sequentially to generate argumentative discourse components, and then produces a final argument conditioned on the components. To further mimic the human writing process and improve the left-to-right generation paradigm of current autoregressive language models, we introduce an argument refinement module that automatically evaluates and refines argument drafts based on feedback received. We evaluate our framework on the task of counterargument generation using a subset of Reddit/CMV dataset. The results show that our method outperforms both end-to-end and chain-of-thought prompting methods and can generate more coherent and persuasive arguments with diverse and rich contents.

## introduction argument generation is an essential task in natural language processing with wide applications, such as debates and essay writing @xcite . in this work, we study counterargument generation which aims to generate persuasive arguments to refute a given proposition on a controversial topic @xcite . however, generating counterarguments poses significant challenges for both humans and machines as it requires a profound comprehension of the original proposition, the ability to present a valid standpoint from an opposing counterargument: proposition: people shouldn't change gender ① ② ③ ④ figure 1 : sample counterargument that refutes the proposition. the argument structure consists of components including 1 a claim serving as the main statement to attack the proposition, 2 a reasoning that supports the claim, 3 a concession responding with potential rebuttals and 4 a conclusion. recent large language models (llms) have exhibited remarkable capabilities in addressing various tasks with human-alike result @xcite @xcite . however, the token-level autoregressive generation paradigm makes llms fall short of dealing with complicated tasks involving multiple actions due to the lack of high-level planning ability @xcite . prior work shows that chain-of-thought (cot) prompting can significantly boost the llms' ability on complex reasoning tasks by encouraging the model to decompose the task into a sequence of intermediate results @xcite . later work further imposes automatic decision-making and action-executing to break down complex tasks leveraging llms @xcite @xcite . although the above methods achieve good performance in solving reasoning tasks, they still face challenges when applied to argument generation. generating arguments not only requires rigorous reasoning but also demands deliberate discourse structures to enhance overall coherence and persuasion @xcite . as shown in figure 1 , a counterargument comprises several discourse components, and generating a strong argument needs both to produce highquality components and to properly organize the components to ensure overall quality. nevertheless, decomposing the goal of argument generation into intermediate actions remains a non-trivial task. moreover, the left-to-right single-pass generation paradigm of current llms hinders them from tracking back and revising in previously generated text. this limitation potentially depletes the soundness and coherence of the generated argument @xcite @xcite . in this work, we propose americano, a novel framework for argument generation with discoursedriven decomposition and agent interaction, where a generation agent first produces an argument draft, and then an evaluation agent and refinement agent iteratively produce feedback and revise the draft. inspired by argumentation theory and argumentative discourse structure @xcite @xcite , our argument generation agent decomposes the goal into predefined actions and sequentially generates each argumentative discourse component. specifically, given a proposition and the goal of generating a counterargument, the sequential actions aiming to create high-quality discourse components include: (1) a claim action that produces a strong claim to refute the proposition; to further mitigate the drawback of left-to-right generation and incorporate feedback, we propose an argument refinement module with two agentsan evaluation agent and a refinement agent. specifically, the argument draft is first evaluated by the evaluate agent to provide verbal feedback signals, and then the feedback is passed to the refinement agent to revise the draft. this process can be conducted iteratively until the evaluator is satisfied with the result. both agents are operated by prompting llms without any model training. this is also akin to the human writing process of first composing a draft and then revising the draft @xcite to improve the quality. we evaluate our framework on the task of zeroshot counterargument generation, with a subset of propositions collected from reddit/cmv dataset. we leverage both llm-based automatic evaluation and human evaluation to validate the model outputs. the results show that our method is able to produce high-quality counterarguments with better coherence and persuasiveness compared with endto-end prompting and cot prompting. moreover, our system can generate more diverse results than baseline methods. data and code are available at: https://github.com/derekkk/llm4arggen . ## argument generation with discourse-driven sequential actions the overall framework is shown in figure 2 , which consists of three agents that collaboratively perform task decomposition and refinement for argument generation. we first introduce the generation agent. argument generation can be modeled as p(y|x), where x is an input proposition and y is an output counterargument. however, directly modeling this probability presents significant challenges, as generating arguments necessitates appropriate high-level planning, rigorous reasoning, and proper content organization. instead of directly prompting llms for argument generation, we decompose the goal into a sequence of actions based on argumentative discourse structure @xcite @xcite . each action tackles a subproblem based on the internal structure of an argument, which typically includes: a claim as the central statement the writer is trying to argue, a reasoning to support the claim, and an optional concession/acknowledgement to address potential dissenters and improve persuasion. 1 driven by this, we break down the generation into sequential actions that first generate the components and then produce a final argument: p(y|x) = p(y|a, r, c, x)p(a|r, c, x)p(r|c, x)p(c|x), where c, r, a denotes claim, reasoning and acknowl- ## argument refinement module previous work has shown that producing an output on a single attempt is challenging for both machines and human beings @xcite @xcite . conventional autoregressive language models produce outputs from left to right at the token level, lacking the capacity to edit and revise previously generated content. drawing inspiration from the human writing process that involves first creating an initial draft and subsequently refining it, we propose an argument refinement module to mimic this process. as illustrated in figure 2 , this module comprises an evaluation agent and a refinement agent. the evaluator first provides feedback on the current draft, and then the refinement agent takes the feedback and revises the draft. two agents interact with each other interatively to formalize an optimization process for generation. ## related work task decomposition and reasoning. llms have achieved impressive results in solving various tasks with prompting @xcite @xcite . however, the token-level left-to-right generation process limits the model's ability to tackle more complex tasks @xcite . to further improve model ability on complex reasoning tasks, recent work involves sampling intermediate reasoning steps @xcite @xcite or decomposing the complicated goal into actions @xcite @xcite . in this paper, we focus on the specific task of argument generation and decompose the goal into a sequence of predefined actions based on the argumentative theory to generate each discourse component. argument generation. argument generation requires text planning, logical reasoning, and content organization @xcite . hua et al. (2019) propose a planning-based model with a retrieval module for counterargument generation. schiller et al. (2021) utilize keywords to control the content of arguments. bao et al. (2022) introduce a dual-decoder model to improve content planning. different from previous work, we leverage llms and introduce a framework with multi-agents for counterargument generation. our method effectively decomposes argument generation into subproblems and prompts llms for each action without model training. ## conclusion in this work, we present a novel framework for argument generation with agent interaction. our framework consists of a generation agent that decomposes argument generation into a sequence of predefined actions driven by argumentative theory to produce a draft, and then a refinement module with an evaluator and a refinement agent to iteratively provide feedback and refine the draft. all parts are implemented leveraging llms with zeroshot prompting. both automatic and human evaluations show that our framework can generate more coherent and persuasive results with better diversity in counterargument generation.