User-Guided Rewrites

• User-guided rewrites are processes where human feedback directs the transformation of text or code, ensuring improved control and interpretability.
• Interactive methods, such as direct typing and auxiliary selection, leverage pre-trained models to offer intelligent suggestions and real-time corrections.
• These systems integrate iterative learning and synthetic data to optimize performance, fairness, and precision in diverse tasks like NLG and SQL optimization.

User-guided rewrites are processes and systems in which human users—acting as annotators, domain experts, or direct end-users—actively drive, correct, or parameterize the transformation of linguistic or symbolic representations. The field encompasses a range of methods in natural language generation, program transformation, query optimization, and interactive learning, where system outputs are steered by explicit user operations, soft constraints, semantic guidance, or dialogic feedback. User-guided rewrites are critical for applications demanding interpretability, personalization, fairness, and controllability, and are increasingly supported by advanced toolchains, neural architectures, and reinforcement learning frameworks.

1. Foundations: User-Guided Revision and Edit Traces

User-guided rewrites emerge from the concept of traceable, anthropogenic editing steps, where each user action—such as an insertion, deletion, replacement, reordering, or morpholexical inflection—is systematically recorded and leveraged for further computation. Systems like ALTER record granular, word-level modification sequences; each revision step is decomposed into atomic edit operations whose composition yields the full rewrite history. Mathematically, word-level salience can be explicitly characterized, e.g.,

$S(X, i) = P(Y|X) - P(Y|X \setminus x_i)$

where $S(X,i)$ evaluates the impact of word $x_i$ on a classification decision. This formalism allows not only the logging of edit operations but also the extraction of human revision patterns, which can serve as direct supervision for generative models that learn editing policies, not just surface realization (Xu et al., 2019).

Detailed revision histories underpin a multitude of tasks (paraphrasing, text simplification, style transfer, gender-aware text transformation), supporting both empirical annotation studies and the derivation of gold references for benchmarking.

2. Interactive and Flexible Editing Interfaces

Modern user-guided rewriting systems provide interactive interfaces empowering nuanced annotator intervention. In platforms such as ALTER, two operation modes are highlighted:

• Direct Typing: Freeform revision by entering a new output.
• Auxiliary Mode: Click-based selection of words, triggering choices among deletion, substitution (supported by suggestions from pre-trained models like BERT), or reordering.

Auxiliary editing is further enhanced via intelligent word or transformation recommendations, either seeded from word embedding similarity or from language-model-based prediction. For example, upon targeting a word for substitution, the system proposes alternatives that maintain semantico-syntactic coherence.

Systems designed for morphologically rich and linguistically complex domains (e.g., Arabic gender rewriting) integrate rule-based analyzers, bigram LLMs for candidate ranking, and sequence-to-sequence neural networks modulated with explicit style/gender side-constraints (Alhafni et al., 2022, Alhafni et al., 2022). This hybridization leverages both the strong local guarantees of rules and the generalization capacity of neural architectures.

3. Real-Time Feedback, Supervision, and Quality Control

A defining property of user-guided rewriting systems is their ability to deliver real-time, multi-level feedback aligned with both task-specific attributes and general text quality metrics:

• Sentence-Level: Measures include Perplexity (PPL), Word Mover Distance (WMD) for content preservation, Edit Distance (ED), and attribute classification (e.g., for gender, style, or fairness).
• Word-Level: Visualizations highlight tokens that are most predictive of undesired attributes via classifier-based salience scoring.

For structured rewriting tasks involving code or logical forms, such as in program transformations or SQL query rewriting, correctness is rigorously formalized:

$\text{in} \backslash \sigma \equiv \text{out}$

guaranteeing semantic preservation under substitutions. Abort-rewrite mechanisms, unequiv context propagation, and binder rules allow logic-based rewriters (e.g., FGL in the ACL2 ecosystem) to flexibly incorporate programmatic or symbolic user guidance, bypassing the constraints of pure logic when needed through extralogical computations and contextual heuristics (Swords, 2020).

In multi-agent plug-and-play systems such as REWRITER for NL2SQL (Ma et al., 22 Dec 2024), feedback is structured as a "check–reflect–rewrite" loop, combining live execution validation, error-type classification, and feedback-weighted action selection, thereby minimizing hallucination risk and aligning more tightly with user intent.

4. Learning from User Preferences, Interaction, and Feedback

A major trajectory is the explicit modeling of user preference and interaction feedback in the rewrite optimization loop:

• Soft Constraint Integration: Features such as continuous/categorical feature scoring, discrete value bounding, and preference-based feature ranking are incorporated via weighted cost objectives and gradient-based updates, as in User Preferred Actionable Recourse (UP-AR) (Yetukuri et al., 2023).
• Direct Human-in-the-Loop Learning: Systems learn to rewrite not only from “gold” reference outputs but from in-situ corrections and follow-up utterances provided by end users. In RLHI (Jin et al., 29 Sep 2025), user-guided rewrites are distilled from dissatisfaction signals and follow-ups, forming preference pairs optimized via persona-conditioned Direct Preference Optimization loss:

$$L_{\text{persona-DPO}} = \mathbb{E}_{u,i} [ \log \sigma ( \beta (\log \frac{\pi_\theta(y^+ | x, p)}{\pi_{\text{ref}}(y^+ | x, p)} - \log \frac{\pi_\theta(y^- | x, p)}{\pi_{\text{ref}}(y^- | x, p)} ) ) ]$$

• Iterative and Adaptive Update Loops: Continuous deployment systems (e.g., IterQR (Chen et al., 16 Feb 2025) in e-commerce) employ an iterative, multi-task learning cycle—spanning retrieval-augmented rewrite generation, online signal collection from implicit user behavior (click logs, conversions), and automatic rewrite promotion or pruning via post-training. This framework closes the loop between user satisfaction and system rewriting, adapting dynamically to domain drift and new behaviors.
• Feedback-Driven Preference Modeling: In recommendation, dialogue, or recourse contexts, collection of preference labels (implicit or explicit) and online update of ranking or reward models are critical. Signal efficacy is often measured via metrics such as Preference Root Mean Squared Error (pRMSE), proximity, sparsity, and relevance to both system and user-defined objectives.

5. Scalable Synthetic Data, Domain Knowledge, and Instructional Alignment

With high-quality human rewrites being costly and sometimes insufficiently informative for retrieval and translation, recent models adopt synthetic or data-driven approaches:

• Synthetic Supervision: Synthetic queries and rewrites, constructed by LLMs (notably GPT-4o) using enriched signals (dialogue, gold doc, answer), more accurately reflect the user’s true informational need, especially in retrieval-augmented generation. Training on such synthetic rewrites yields superior retrieval (MRR), generation (ROUGE), and intent-alignment compared to human-produced references (Zheng et al., 26 Sep 2025).
• Instructional Tuning and Reinforcement Learning: Systems such as RewriteLM (Shu et al., 2023) and SynRewrite (Zheng et al., 26 Sep 2025) use open-domain edit corpora (Wikipedia, C4, etc.) to generate diverse instructions and train reward models capable of ranking rewrites by content fidelity, degree of change (edit ratio), and consistency (NLI-based metrics). Reinforcement learning methods (such as DPO or APO) are then employed to optimize models not just for surface similarity but for downstream task performance.
• Continual Domain Pre-Training: In highly technical verticals (finance, medicine, law), continual pre-training (CPT) of rewriter models on professional documents bridges gaps between lay queries and domain-specific document language, improving retrieval and QA robustness in domain-specialized RAG systems (Wang et al., 1 Jul 2025).

6. Application Domains and Practical Impact

User-guided rewrite paradigms have wide-ranging applications across NLP, databases, and decision-support systems:

• Natural Language Generation: Paraphrasing, style transfer, fairness and bias mitigation, and fine-grained demographic adaptation (e.g., gender rewriting in Arabic)—with rigorous evaluation such as M² F₀.₅ and BLEU.
• Conversational Assistants and Dialogue Agents: Query rewriting for robust intent recovery in the presence of recognition errors (ASR/NLU), pointer-generator architectures with personalized user memory, and recovery from user-initiated repetitions (Roshan-Ghias et al., 2020, Nguyen et al., 2021).
• Search and Retrieval: Enhanced product and information discovery in e-commerce and QA via modeling transitional queries, LLM-based reformulation, and intent flow tracking with mining of large-scale interaction logs and controlled LLM alternators (Yetukuri et al., 25 Jul 2025).
• Program and Query Optimization: SQL and code rewrites leveraging LLMs with user-guided, evidence-rich retrieval and stepwise application of rewrite recipes, as in R-Bot (Sun et al., 2 Dec 2024) and LITHE (Dharwada et al., 18 Feb 2025). Hallucination risk is reduced with evidence-based filtering, Monte Carlo tree search guided by token probabilities, and explicit cost evaluation.

Table: Example User-Guided Rewrite Mechanisms Across Domains

Domain	User-Guided Mechanism	Key Metric(s)
NLG (paraphrasing)	Word-level revision + feedback	Content preservation (WMD)
Conversational search	Rewrite-then-edit via LLM	Retrieval MRR, NLI
SQL optimization	Evidence-driven step-by-step LLM	Query latency/improvement
Recourse/decision	Soft-constraint gradient descent	pRMSE, redundancy, proximity

7. Challenges, Limitations, and Future Directions

Persistent challenges include scaling user-guided rewrites across diverse domains, managing ambiguity and under-specification, and preventing error propagation during rewrite or fusion. The cost and sparsity of annotated supervision, particularly for niche domains or complex structural rewrites (e.g., code or queries), is partly mitigated by synthetic data, iterative self-improvement, and open feedback collection.

Future directions pointed out in recent works involve:

• Enhanced self-reflection and experience accumulation for automated reflectors and rewriters (Ma et al., 22 Dec 2024), targeting better schema adaptation in data-to-text and NL2SQL systems.
• Balancing robustness and efficiency in multi-round rewriting, active selection of candidate rewrites, and online deployment efficiency.
• Integrating rewrite feedback with proactive learning in ongoing deployments, closing the loop from user persona and feedback to system update, as demonstrated in RLHI (Jin et al., 29 Sep 2025).
• Extension to multimodal and cross-lingual contexts, as well as proactive detection of privacy or security leakage in synthetic rewrites.

Integration of user-guided dimensions into rewriting models evidently leads to increased personalization, control, and alignment between system outputs and user preferences or domain constraints, supporting both automation and interpretability in real-world deployments.