LLM-guided headline rewriting for clickability enhancement without clickbait
arXiv:2603.22459v1 Announce Type: new Abstract: Enhancing reader engagement while preserving informational fidelity is a central challenge in controllable text generation for news media. Optimizing news headlines for reader engagement is often conflated with clickbait, resulting in exaggerated or misleading phrasing that undermines editorial trust. We frame clickbait not as a separate stylistic category, but as an extreme outcome of disproportionate amplification of otherwise legitimate engagement cues. Based on this view, we formulate headline rewriting as a controllable generation problem, where specific engagement-oriented linguistic attributes are selectively strengthened under explicit constraints on semantic faithfulness and proportional emphasis. We present a guided headline rewriting framework built on a large language model (LLM) that uses the Future Discriminators for Generation (FUDGE) paradigm for inference-time control. The LLM is steered by two auxiliary guide models: (1
arXiv:2603.22459v1 Announce Type: new Abstract: Enhancing reader engagement while preserving informational fidelity is a central challenge in controllable text generation for news media. Optimizing news headlines for reader engagement is often conflated with clickbait, resulting in exaggerated or misleading phrasing that undermines editorial trust. We frame clickbait not as a separate stylistic category, but as an extreme outcome of disproportionate amplification of otherwise legitimate engagement cues. Based on this view, we formulate headline rewriting as a controllable generation problem, where specific engagement-oriented linguistic attributes are selectively strengthened under explicit constraints on semantic faithfulness and proportional emphasis. We present a guided headline rewriting framework built on a large language model (LLM) that uses the Future Discriminators for Generation (FUDGE) paradigm for inference-time control. The LLM is steered by two auxiliary guide models: (1) a clickbait scoring model that provides negative guidance to suppress excessive stylistic amplification, and (2) an engagement-attribute model that provides positive guidance aligned with target clickability objectives. Both guides are trained on neutral headlines drawn from a curated real-world news corpus. At the same time, clickbait variants are generated synthetically by rewriting these original headlines using an LLM under controlled activation of predefined engagement tactics. By adjusting guidance weights at inference time, the system generates headlines along a continuum from neutral paraphrases to more engaging yet editorially acceptable formulations. The proposed framework provides a principled approach for studying the trade-off between attractiveness, semantic preservation, and clickbait avoidance, and supports responsible LLM-based headline optimization in journalistic settings.
Executive Summary
This article presents a novel framework for enhancing news headline clickability without resorting to clickbait by leveraging LLMs through a controllable generation paradigm. Rather than treating clickbait as a stylistic outlier, the authors conceptualize it as an extreme manifestation of disproportionate amplification of legitimate engagement cues. The proposed system integrates two auxiliary guide models—a clickbait scoring model for negative guidance and an engagement-attribute model for positive guidance—both trained on neutral headlines. Using the FUDGE paradigm, the LLM generates headlines along a spectrum from neutral to engaging formulations, calibrated via adjustable guidance weights at inference. The framework offers a principled, scalable method for balancing editorial integrity with reader engagement, directly addressing a critical tension in journalistic content optimization.
Key Points
- ▸ Conceptualization of clickbait as an extreme outcome of disproportionate amplification
- ▸ Utilization of dual guide models (clickbait scoring and engagement-attribute) for dual constraint enforcement
- ▸ Application of FUDGE paradigm for inference-time control via adjustable guidance weights
Merits
Innovative Framework
The authors effectively reframe clickbait as a controllable outcome rather than a categorical anomaly, enabling a nuanced, principled approach to headline optimization.
Methodological Precision
The integration of dual constraint-based guidance models, combined with synthetic synthetic variant generation, provides a robust, empirically grounded structure for evaluating trade-offs.
Demerits
Complexity of Implementation
Training and calibrating dual auxiliary models on neutral headlines may introduce computational overhead and require high-quality, representative corpora, potentially limiting scalability.
Subjectivity in Guidance Weights
Determining optimal weights for guidance at inference may involve interpretive subjectivity, potentially affecting consistency across editorial contexts.
Expert Commentary
This paper makes a significant contribution to the intersection of AI-generated content and journalistic ethics. The conceptual shift from viewing clickbait as a binary category to a gradient outcome of amplification dynamics is profound. The authors’ use of dual constraint models—one inhibitory, one promotional—creates a balanced mechanism that mirrors the dual responsibilities of media: informing and engaging. Moreover, the decision to train guides on neutral corpora ensures grounding in editorial integrity, avoiding the risk of amplifying bias. The FUDGE paradigm’s applicability here is particularly noteworthy, as it transforms LLMs from passive generators into active, controllable agents in content curation. This work not only advances technical capabilities but also sets a new standard for responsible AI in journalism. It is a timely, impactful contribution that bridges the gap between innovation and accountability.
Recommendations
- ✓ Journalistic organizations should pilot this framework in controlled editorial environments to assess effectiveness in real-world content workflows.
- ✓ Academic and media ethics institutions should convene workshops to evaluate the framework’s applicability across diverse media ecosystems and cultural contexts.
Sources
Original: arXiv - cs.CL
