Argument Improvement (ArgImp)

• Argument Improvement (ArgImp) is the process of rewriting argumentative text to boost quality metrics like clarity, conciseness, and logical coherence.
• It utilizes techniques such as claim optimization, iterative refinement, and sufficiency-aware diffusion to ensure changes retain the original stance.
• Evaluation involves metrics like ROUGE, BERTScore, and human judgments to assess improvements in persuasiveness, faithfulness, and overall argument quality.

Searching arXiv for the cited works to ground the article in current literature. Search query: "Arg-LLaDA Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement" Argument Improvement (ArgImp) denotes a family of tasks and systems that seek to improve argumentative text while preserving its core stance and content as much as possible. In the most explicit formalization, ArgImp is defined as rewriting an existing monological argumentative text $A$ into $A'$ so as to increase its intrinsic argumentative quality $Q$, with improvement holding when $Q(A') > Q(A)$ (Huber et al., 18 Sep 2025). Across the literature, this broad objective appears in several operational forms: claim rewriting and optimization, iterative summary refinement, controllable factual argument generation, pedagogical guidance for argument construction, argument-aware summarization, convincingness-oriented appraisal design, and automated feedback for student writing (Skitalinskaya et al., 2022, Li et al., 25 Jul 2025, Saha et al., 2023, Silva et al., 2015, Elaraby et al., 2022, Greschner et al., 10 Nov 2025, Favero et al., 20 Feb 2025). The unifying concern is not generic paraphrase, but systematic improvement along dimensions such as faithfulness, coverage, conciseness, clarity, coherence, logical sufficiency, and resistance to criticism.

1. Conceptual foundations and quality criteria

ArgImp is defined most formally in the CLEAR framework as a rewriting task focused on intrinsic argumentative quality rather than extrinsic audience response. The rewrite is constrained by preserving stance and core content, preferring transformations over wholesale additions or deletions, and improving persuasion and coherence in the output (Huber et al., 18 Sep 2025). A closely related formulation appears in claim optimization, where the task is to rewrite an input claim $c$ into an output claim $\tilde{c}$ that improves text and argument quality while preserving the original meaning as far as possible (Skitalinskaya et al., 2022). In summarization-oriented work, the same improvement objective is expressed as generating concise, structured summaries from claim–evidence pairs while maximizing comprehensiveness, faithfulness, and conciseness, formalized as

$S^* = \arg\max_S Quality(S \mid \{(c_i, E_i)\})$

(Li et al., 25 Jul 2025).

The quality dimensions associated with ArgImp are heterogeneous but strongly recurrent. Claim optimization targets clarity, fluency, relevance, self-containedness, specificity, and argument quality in general (Skitalinskaya et al., 2022). Arg-LLaDA frames improvement around faithfulness to evidence, comprehensive coverage of claims, and conciseness, and operationalizes failure cases as unsupported, redundant, or incomplete spans (Li et al., 25 Jul 2025). The ARG teaching platform states that its Toulmin-based workflow produces arguments that are “more concise, precise, minimally structured and more resistant to criticism” (Silva et al., 2015). In legal summarization, improvement consists in better capturing issues, reasons, and conclusions rather than narrative background (Elaraby et al., 2022). In educational assessment, quality is discretized into Ineffective, Adequate, and Effective, with “Effective” defined as well-structured, clear, and strongly supporting the argument (Favero et al., 20 Feb 2025).

A further distinction concerns intrinsic versus audience-centered notions of improvement. CLEAR explicitly emphasizes intrinsic text-focused quality (Huber et al., 18 Sep 2025). By contrast, the appraisal-based convincingness study treats convincingness as inherently subjective and dependent on recipient goals, standards, prior knowledge, experiences, and stance; it therefore links improvement to appraisal dimensions such as pleasantness, familiarity, positive consequentiality, and norm alignment (Greschner et al., 10 Nov 2025). This suggests that ArgImp has at least two major interpretive regimes: one centered on the internal organization and linguistic quality of arguments, and one centered on predicted effects on a receiver.

2. Rewriting and optimization as direct argument improvement

The most direct ArgImp paradigm is explicit rewriting. In claim optimization, the proposed pipeline first generates a diverse set of candidate rewrites using BART-large and then selects the best candidate with AutoScore, a weighted combination of fluency, meaning preservation, and argument quality:

$$S(\tilde{c}) = \alpha \cdot fluency(\tilde{c}) + \beta \cdot meaning(\tilde{c}) + \gamma \cdot argument(\tilde{c}),$$

with $\alpha=0.43$, $\beta=0.01$, and $A'$0 after grid search (Skitalinskaya et al., 2022). Candidate generation uses $A'$1 outputs, combining top-1 with top-$A'$2 sampling, and reported decoding settings include length penalty $A'$3, no-repeat n-gram size $A'$4, temperature $A'$5, min length $A'$6, and max length $A'$7 (Skitalinskaya et al., 2022). On the reported English-language corpus, the system improves 60% of all claims while worsening 16% only, and common optimization types include specification, simplification, reframing, elaboration, corroboration, neutralization, disambiguation, and copy editing (Skitalinskaya et al., 2022).

Instruction tuning provides a second rewriting-oriented route. ArgInstruct does not use the exact label “Argument Improvement,” but includes four closely aligned tasks from “Learning From Revisions”: Claim Revision Improvement, Suboptimal Claim Detection, Claim Improvement Suggestions, and Claim Optimization (Stahl et al., 28 May 2025). These tasks jointly cover diagnosis, suggestion, and generation. The instruction-tuned model is trained on 52k CA-related instructions synthesized from 105 seed tasks, with standard next-token cross-entropy over outputs,

$A'$8

and uses LoRA with $A'$9, $Q$0, dropout $Q$1, 7 epochs, learning rate $Q$2, effective batch size 64, cosine LR decay, and warmup ratio 0.05 (Stahl et al., 28 May 2025). On improvement-oriented seen tasks, reported scores include F1 $Q$3 for Claim Revision Improvement, F1 $Q$4 for Suboptimal Claim Detection, F1 $Q$5 for Claim Improvement Suggestions, and ROUGE-L F1 $Q$6 for Claim Optimization (Stahl et al., 28 May 2025).

A broader linguistic view of rewriting is provided by CLEAR, which evaluates LLM-based argument rewriting with 57 metrics across lexical, syntactic, semantic, and pragmatic levels, plus 4 argument-component metrics (Huber et al., 18 Sep 2025). The observed behavior is that models perform ArgImp by shortening texts while simultaneously increasing average word length and merging sentences, and overall they increase persuasion and coherence (Huber et al., 18 Sep 2025). Reported length changes were negative for longer corpora, such as Rev1 $Q$7, Rev2 $Q$8, Rev3 $Q$9, and Essays $Q(A') > Q(A)$0, while Microtexts increased in length by $Q(A') > Q(A)$1 (Huber et al., 18 Sep 2025). This shows that rewriting-based ArgImp is not a single operation but a family of controlled transformations, often favoring compaction, lexical densification, and discourse simplification.

3. Iterative refinement, sufficiency, and selective correction

A distinct ArgImp paradigm replaces one-shot rewriting with iterative refinement. Arg-LLaDA introduces a large language diffusion framework in which summaries are iteratively improved via sufficiency-guided remasking and regeneration (Li et al., 25 Jul 2025). The model retains the masked diffusion architecture and reverse denoising process of LLaDA, starting from a fully masked summary $Q(A') > Q(A)$2 and iteratively sampling cleaner states

$Q(A') > Q(A)$3

where $Q(A') > Q(A)$4, and outputs $Q(A') > Q(A)$5 (Li et al., 25 Jul 2025). Training uses denoising score matching for masked infilling,

$Q(A') > Q(A)$6

while the sufficiency signals themselves are applied only at inference (Li et al., 25 Jul 2025).

The central diagnostic mechanism is a sufficiency-checking module that combines few-shot chain-of-thought prompting with a supervised RoBERTa-base classifier,

$Q(A') > Q(A)$7

(Li et al., 25 Jul 2025). Its outputs are transformed into token-level sufficiency scores $Q(A') > Q(A)$8, and the masking controller remasks the top-$Q(A') > Q(A)$9 proportion of tokens according to

$c$0

Unsupported, redundant, or incomplete spans are thus preferentially regenerated, while coherent segments are preserved (Li et al., 25 Jul 2025). The algorithmic loop explicitly alternates diagnosis, remasking, denoising, and reassessment.

The reported empirical effect is that iterative sufficiency-aware refinement improves both automatic and human evaluation. On ArgKP, moving from 0 to 3 iterations raises ROUGE-L from 0.429 to 0.453, BLEURT from 0.629 to 0.658, and BERTScore from 0.814 to 0.834; beyond 3 iterations, returns diminish (Li et al., 25 Jul 2025). In ablations, “Combined (CoT + classifier)” yields the best scores, with R-L 0.453, BLEURT 0.658, and BERTScore 0.834, outperforming “No diagnosis,” “CoT only,” and “Classifier only” (Li et al., 25 Jul 2025). Human evaluation reports Coverage 4.7, Faithfulness 4.6, and Conciseness 4.4, compared with 4.5 / 4.4 / 4.2 for Gemini 2.5 and 4.4 / 4.3 / 4.1 for DeepSeek-R1 (Li et al., 25 Jul 2025). In this setting, ArgImp is not merely better generation; it is diagnosis-guided correction under explicit evidence-grounding constraints.

A related principle appears in classification pipelines that route hard cases to stronger models. In compact-language-model argument classification, a BERT classifier is augmented with selective GPT-4 refinement for low-confidence items, routing about 20% of Args.me samples and about 25% of US2016 samples to GPT-4 (Pietron et al., 2024). The approach yields large improvements, including F1 gains from 62.0 to 72.5 on US2016 and from 57.7 to 68.5 on UKP (Pietron et al., 2024). Although the target task is classification rather than rewriting, the method embodies the same ArgImp principle: detect probable failure, then apply targeted corrective processing rather than uniform second-pass generation.

4. Structure-aware generation, summarization, and formal reasoning

Another major line of ArgImp improves arguments by making structure explicit during generation. ArgU is a BART-based factual argument generator controlled by stance tokens, <pro> and <con>, and Walton scheme control codes such as <from_consequence>, <from_source_authority>, <from_source_knowledge>, <goal_from_means/means_for_goal>, and <rule_or_principle> (Saha et al., 2023). The dual-step variant first generates an argument template and then realizes a final argument, formalized as

$c$1

with beam size 5, maximum generation length 50, and trigram repetition penalty (Saha et al., 2023). On the reported test set, ArgU-Dual achieves BLEU 0.158, ROUGE-L 0.381, Fact 0.641, Entail 0.406, and Contra 0.144, while human evaluation gives it 4.86 fluency, 0.80 stance appropriateness, 0.83 scheme appropriateness, 3.88 fact faithfulness, and 4.06 logical coherence (Saha et al., 2023). This establishes a version of ArgImp in which better arguments arise from explicit control over inference patterns, stance, and factual grounding.

In legal summarization, argument-role labeling provides a structurally different but related mechanism. ArgLegalSumm classifies sentences into Issue, Reason, Conclusion, or non-IRC using LegalBERT, then injects predicted roles into a summarizer via special tokens such as <IRC>, <Issue>, <Reason>, and <Conclusion> (Elaraby et al., 2022). The argument classification loss is

$c$2

and the summarization loss is standard sequence-to-sequence cross-entropy,

$c$3

On the legal case summarization test set, vanilla LED-base achieves ROUGE-1/2/L of 49.56 / 22.75 / 46.48, whereas arg-LED-base with predicted 6 markers reaches 50.23 / 26.29 / 47.49 (Elaraby et al., 2022). The gains are interpreted as increased argumentativeness and more concise, focused summaries (Elaraby et al., 2022). Here, ArgImp is achieved not by changing the decoder objective, but by making argumentative roles legible to the generator.

Formal computational argumentation offers yet another structure-centered path. MQArgEng uses abstract argumentation semantics to generate multiple candidate arguments, detect conflicts, construct an argumentation framework $c$4, compute accepted arguments via ASPARTIX, and condition the final LLM response on the grounded or preferred extension (Castagna et al., 2024). The framework uses standard Dung semantics, including conflict-free sets, defence, admissible sets, complete extensions, and the grounded extension as the least fixpoint of the characteristic function $c$5 (Castagna et al., 2024). On MT-Bench, the pipeline raises the overall average from 5.96 to 6.09, with gains in reasoning, coding, extraction, STEM, and humanities, although writing and roleplaying slightly decline (Castagna et al., 2024). This suggests that one form of argument improvement consists in filtering generated content through formal acceptability semantics before final synthesis.

5. Pedagogical, educational, and receiver-oriented perspectives

ArgImp also appears as a pedagogical and assessment framework. The ARG system is an online multilingual platform grounded in Toulmin’s theory and designed primarily for Law, though it also targets philosophy, literature, and the sciences (Silva et al., 2015). Its workflow begins with realistic case prompts, guides users through fields corresponding to Claim, Grounds, Warrant, and Backing, generates a structured text, and then subjects it to verification checklists, argument schemes, critical questions, and community and moderator evaluation (Silva et al., 2015). Improvement is evaluated along “Clarity of the claim,” “Necessary and sufficient data to support the conclusion,” “Relevant support/proof for the case,” and “Explicit strength of the conclusion and acknowledgment of possible refutations” (Silva et al., 2015). In a 2014 pilot with 206 undergraduate law students, the resulting arguments were reported to be “remarkably more concise, precise, minimally structured and more resistant to criticism,” although the authors explicitly describe the study as preliminary and note the need for more tests (Silva et al., 2015).

In educational NLP, small open-source LLMs are used to identify, classify, and assess arguments in student essays so as to generate targeted feedback. The pipeline covers segmentation into argument components, classification into seven types—Lead, Position, Claim, Counterclaim, Rebuttal, Evidence, and Concluding Statement—and quality assessment with the three-level rubric Ineffective, Adequate, Effective (Favero et al., 20 Feb 2025). Fine-tuned Llama 3.1 8B reaches macro F1 = 87.52 for segmentation, while fine-tuned Gemma 2 9B reaches macro F1 = 79.74 for type classification with gold segmentation; for quality assessment with gold segmentation, Gemma 2 9B in three-shot prompting reaches macro F1 = 44.56 (Favero et al., 20 Feb 2025). The paper frames the educational use case as delivering actionable feedback such as identifying missing claims, weak evidence, absent counterclaims or rebuttals, and unclear conclusions (Favero et al., 20 Feb 2025). This positions ArgImp as formative intervention rather than only model-side rewriting.

A receiver-oriented perspective is developed in the appraisal-based convincingness study. Using the ContArgA corpus of 800 arguments annotated by five participants each, for 4,000 contextualized annotations, the paper compares categorical emotions with appraisals for predicting subjective convincingness on a 1–5 scale (Greschner et al., 10 Nov 2025). In pipeline experiments using gold annotations, appraisals produce stronger gains than categorical emotions for some models; for example, LLaMA improves from a text-only baseline Spearman’s $c$6 of .27 to .42 with gold appraisals, a gain of +.15 (Greschner et al., 10 Nov 2025). The paper’s practical ArgImp guidance is to increase pleasantness, positive consequentiality, familiarity, and norm alignment while reducing unpleasantness and norm violations (Greschner et al., 10 Nov 2025). This does not define improvement in purely textual terms. Instead, it makes improvement audience-relative, pathos-sensitive, and appraisal-driven.

6. Evaluation regimes, limitations, and open directions

Evaluation in ArgImp is methodologically plural. Claim optimization uses BLEU, ROUGE-L, SARI, exact-match accuracy, NoEd, and human judgments of fluency, meaning preservation, and argument quality (Skitalinskaya et al., 2022). Arg-LLaDA combines ROUGE, BERTScore, BLEURT, and human ratings for Coverage, Faithfulness, and Conciseness (Li et al., 25 Jul 2025). CLEAR expands evaluation to 57 metrics across lexical, syntactic, semantic, and pragmatic levels, including Average Word Length, Flesch Reading Ease, BERTAlign sentence operations, RST tree depth, GRUEN, and LLM-graded coherence and persuasion (Huber et al., 18 Sep 2025). Educational argument mining relies on macro-F1 over segmentation, type, and quality classes, using overlap-based span matching rules (Favero et al., 20 Feb 2025). Together, these frameworks show that ArgImp has no single canonical metric: different operationalizations privilege different quality notions.

CLEAR is especially notable because it turns argument rewriting into a multi-level empirical object. Its lexical metrics include formulas such as

$c$7

and its percent change convention is

$c$8

It reports that improved texts were manually preferred about 79% of the time, with inter-reviewer agreement about 65.8% (Huber et al., 18 Sep 2025). At the same time, the framework identifies trade-offs: shorter texts with longer words often yield higher persuasion and coherence but lower reading ease, and models may overcompress, neutralize tone, or lose nuance (Huber et al., 18 Sep 2025). This makes explicit that improvement is not monotone across all linguistic levels.

The literature also converges on several limitations. Arg-LLaDA notes overcompression risks if $c$9 is too high, residual hallucinations when evidence is sparse or mislabeled, and computational overhead from inference-time sufficiency diagnosis (Li et al., 25 Jul 2025). Claim optimization reports that the system adds less evidence than humans do, and that models sometimes introduce repetitive or unsound conclusions (Skitalinskaya et al., 2022). ArgU documents occasional lack of understanding, especially around negation, and template modification that can alter intended meaning in the second stage (Saha et al., 2023). Educational assessment highlights annotation-quality and rubric-consistency issues, especially for quality labels (Favero et al., 20 Feb 2025). The appraisal study explicitly warns that inferring emotions or appraisals may enable manipulative arguments and recommends safeguards (Greschner et al., 10 Nov 2025). Formal-reasoning augmentation via MQArgEng does not solve factuality if all candidate arguments are hallucinated (Castagna et al., 2024).

Several future directions recur across papers. Arg-LLaDA suggests stronger sufficiency models with multi-label judgments, controllers operating on sentences or argument units, and integration with retrieval or formal reasoning (Li et al., 25 Jul 2025). ArgInstruct points toward broader CA coverage and stronger evaluation of generated improvements beyond ROUGE-L (Stahl et al., 28 May 2025). CLEAR proposes human-in-the-loop ArgImp, multilingual expansion, richer pragmatic metrics, and content-preservation checks (Huber et al., 18 Sep 2025). ArgLegalSumm suggests joint learning, role-conditioned attention, and coverage constraints to ensure Issue–Reason–Conclusion presence (Elaraby et al., 2022). A plausible implication is that future ArgImp systems will increasingly combine controlled generation, explicit diagnostic modules, discourse or argument-structure signals, and audience-sensitive evaluation, rather than relying on a single end-to-end rewriting model.