✍️ Text Generation¶

💬 ACL2026 · 17 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (12) · 🧪 ICML2026 (2) · 🤖 AAAI2026 (3) · 📹 ICCV2025 (1) · 🧪 ICML2025 (1) · 💬 ACL2025 (27)

🔥 Top topics: Summarization ×5 · LLM ×3 · Agents ×2

Frankentext: Stitching Random Text Fragments into Long-Form Narratives: This paper proposes the Frankentext paradigm, which enables LLMs to stitch random human text fragments into coherent long-form narratives under extreme constraints (90% of text copied verbatim from human writing). This reveals the severe failure of current AI text detectors in mixed-authorship scenarios (72% of Frankentext is misclassified as human writing).
ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline: This paper introduces ConlangCrafter, an LLM-based multi-hop pipeline that decomposes constructed language (conlang) design into modular stages of phonology, grammar, and lexicon. It ensures typological diversity through randomness injection and internal consistency via self-refinement loops, while proposing an automated evaluation framework encompassing typological diversity analysis and translation consistency.
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration: This paper presents XtraGPT—the first open-source LLM suite (1.5B–14B) specifically for academic paper revision. By fine-tuning on 7,000 top-tier conference papers and 140,000 standard-guided instruction-revision pairs, it achieves context-aware paragraph-level controllable revisions. The 7B version matches GPT-4o-mini, while the 14B version outperforms GPT-4o-mini. Human evaluations show an average increase of 0.65 points in predicted paper scores after revisions.
In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis: This paper proposes the "Scientific Impact Summarization" task: first identifying fine-grained intents that truly reveal impact from the citation contexts of a paper, and then generating an impact narrative that evolves over time. This approach better illustrates how a paper is adopted, criticized, and transformed by subsequent work compared to simple citation counts.
Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search: This paper proposes PACO, which reformulates "multi-attribute controllable summarization" as a planning problem to find an "attribute control sequence." Using a customized Monte Carlo Tree Search (where nodes are full summaries and actions are single-attribute adjustments), it identifies the optimal adjustment path during the prompting stage without any attribute-specific training. With Llama-3.2-1B, it achieves controllability comparable to the Llama-3.3-70B baseline, while Llama-3.3-70B + PACO surpasses all existing methods.
Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style: The authors conducted a pre-registered online study with 81 participants who used GPT-o4-mini to draft and then manually post-edit style-sensitive texts such as wedding vows and apology letters. The findings reveal that while post-editing significantly moves the text toward the user's personal style and away from the LLM's style, the edited texts still systematically retain more "AI-like" traces than independent writing—a residue that participants themselves fail to perceive.
Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety: The authors utilized 2,580 stories generated by GPT-4o / Llama-3.3-70B corresponding to the UFLI K–2 English reading curriculum to perform four SFT designs (baseline, Good Stories, Rewarded SFT, and simulated children's pronunciation errors) on three 8B models (Llama 3 / Granite 3.3 / Apertus). The results demonstrate that compact models + appropriate SFT strategies can outperform zero-shot GPT-4o and Llama-3.3-70B on key K-2 metrics such as Spache readability, syntactic complexity, and toxicity. Among these, Rewarded SFT proved most stable and nearly hallucination-free.
EDUMATH: Generating Standards-aligned Educational Math Word Problems: The authors systematize the task of "generating math word problems (MWP) aligned with K-12 math curriculum standards," collecting 11,000+ STEM MWP training data points annotated by real US teachers. Through an SFT + KTO + ModernBERT filtering pipeline, they trained two open-source SOTA generators, EDUMATH-12B/30B. They conducted the first RCT on actual 3rd-5th grade students, finding that while student accuracy was comparable between LLM-generated and human-written problems, students showed an almost unanimous preference for customized LLM problems.
FACTS: Table Summarization via Offline Template Generation with Agentic Workflows: Ours proposes FACTS (Fast, Accurate, and Privacy-Compliant Table Summarization), which automatically generates reusable offline templates (SQL queries + Jinja2 templates) through a three-stage Agentic workflow. It achieves rapid, accurate, and privacy-compliant query-focused table summarization, outperforming baselines across FeTaQA, QTSumm, and QFMTS benchmarks.
Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models: This paper trains BERT, GPT-2, TurnGPT, LLaMA-3 8B, and Qwen-3 8B on English and Japanese spoken dialogue corpora using three fine-tuning tasks: MASK, NTP, and TTP. It utilizes t-SNE visualization and silhouette clustering to quantify the representation quality of "backchannels" (e.g., uh-huh) and "fillers" (e.g., um). The study finds that fine-tuning significantly distinguishes these "semantically bleached" functional words within the embedding space and enables models to naturally generate diverse backchannels/fillers during NLG, marking a quantifiable step toward "human-like conversational LMs."
Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering: This paper systematically investigates the representation mechanisms and intrinsic correlations of emotion and rhetorical neurons in LLMs. By proposing a neuron recognition framework combined with multi-dimensional screening and an adaptive masking verification method, it achieves directional induction of emotion/rhetoric prediction and utilizes rhetorical neurons to assist emotion recognition.
Difficulty-Controllable Cloze Question Distractor Generation: This paper proposes DCDG, which enables easy/hard difficulty control for cloze distractor generation via dual-path data augmentation, QA ensemble difficulty clustering, and multi-task seq2seq training, significantly outperforming GPT-4o in both automatic and human evaluations.
Losses that Cook: Topological Optimal Transport for Structured Recipe Generation: This paper proposes a topological loss function based on Sinkhorn divergence that represents ingredient lists as point clouds in embedding space. By minimizing the geometric discrepancy between predicted and ground-truth ingredients, it significantly improves ingredient recall and quantity accuracy in structured recipe generation, being preferred in 62% of human evaluations.
Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification: The Re-RIGHT framework is proposed, utilizing a 4B policy model trained via GRPO with a three-module reward (lexical coverage + semantic preservation + coherence). It achieves precise text simplification across English, Japanese, Korean, and Chinese according to learner proficiency levels (CEFR/JLPT/TOPIK/HSK), outperforming large models like GPT-5.2 and Gemini 2.5.
SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization: Ours proposes SCURank, a ranking framework based on Summary Content Units (SCUs). It ranks candidate summaries by extracting SCUs, estimating information importance through cross-summary clustering, and scoring based on informativeness. This replaces unstable direct LLM ranking and coarse-grained ROUGE ranking. In multi-LLM distillation scenarios, combined with BRIO contrastive learning, it significantly improves the summarization performance of distilled models.
ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts: This paper proposes ThreadSumm, a multi-stage LLM pipeline framework that models nested discourse thread summarization as a hierarchical reasoning problem. It first extracts aspects and Atomic Content Units (ACUs) for content planning, constructs thread-aware sequences through sentence ordering, and finally utilizes Tree of Thoughts (ToT) search to generate and score multiple paragraph candidates. The method outperforms baselines on Reddit and StackExchange datasets.
Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation: This paper proposes the PLOTTER framework, which signifies the first shift of narrative planning from textual representations to graph structures (Event Graph + Character Graph). Through a multi-agent Evaluate-Plan-Revise iterative loop, narrative defects are diagnosed and repaired on graph topologies, significantly outperforming existing methods in dimensions such as narrativity, characterization, and dramatic tension.