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With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--``write like me&#39;&#39;--has become a rapidly growing area of interest. However, style personalization is highly specific, relative to every user, and depends strongly on the pragmatic context, which makes it uniquely challenging. Although prior research has introduced benchmarks and metrics for this area, they tend to be non-standardized and have known limitations (e.g., poor correlation with human subjects). LLMs have been found to not capture author-specific style well, it follows that the metrics themselves must be scrutinized carefully. In this work we critically examine the effectiveness of the most common metrics used in the field, such as BLEU, embeddings, and LLMs-as-judges. We evaluate these metrics using our proposed style discrimination benchmark, which spans eight diverse writing tasks across three evaluation settings: $\textit{domain discrimination}$, $\textit{authorship attribution}$, and $\textit{LLM-generated personalized vs non-personalized discrimination}$. We find strong evidence that employing ensembles of diverse evaluation metrics consistently outperforms single-evaluator methods, and provide guidance on how to reliably assess style-personalized text generation.

ACL 2026

Evaluating Style-Personalized Text Generation: Challenges and Directions

style evaluation

style personalized text generation

text style transfer

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--``write like me''--has become a rapidly growing area of interest. However, style personalization is highly specific, relative to every user, and depends strongly on the pragmatic context, which makes it uniquely challenging. Although prior research has introduced benchmarks and metrics for this area, they tend to be non-standardized and have known limitations (e.g., poor correlation with human subjects). LLMs have been found to not capture author-specific style well, it follows that the metrics themselves must be scrutinized carefully. In this work we critically examine the effectiveness of the most common metrics used in the field, such as BLEU, embeddings, and LLMs-as-judges. We evaluate these metrics using our proposed style discrimination benchmark, which spans eight diverse writing tasks across three evaluation settings: $\textit{domain discrimination}$, $\textit{authorship attribution}$, and $\textit{LLM-generated personalized vs non-personalized discrimination}$. We find strong evidence that employing ensembles of diverse evaluation metrics consistently outperforms single-evaluator methods, and provide guidance on how to reliably assess style-personalized text generation.

workshop paper

Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer from quality issues stemming from annotator misunderstanding or lack of engagement. To address this, we introduce a real-time requirement adherence (RE-AD) framework that leverages LLMs to proactively validate labeling quality. Our methodology involves decomposing Standard Operating Procedures (SOPs) into atomic rules via self-reflection, categorizing them by complexity, and applying tiered validation strategies. Evaluated on a synthetic benchmark, the system achieved an F1 score of 0.749. Furthermore, production deployment resulted in annotators accepting and fixing 82% of the errors flagged by the framework. We include ablation studies to demonstrate the impact of our core design decisions.

RE-AD: Real-Time Requirement Adherence for Data Labeling

General-purpose language models are trained to produce varied natural language outputs, but for some tasks, like annotation or classification, we need more specific output formats. LLM systems increasingly support structured output, which enforces formats by sampling tokens according to a grammar — but also unpredictably reduces downstream performance. Are there systematic differences between grammars that appear semantically (and often visually) similar to humans? To answer this, we test four popular model families with five varying output formats on four common NLP benchmarks. We find all models perform most accurately when guided to use formats respecting convention, such as letters for multiple choice and real numbers for numerical prediction. Performance also improves by 5%-10% when guiding models to return tokens incorporating leading whitespace, with smaller models benefiting the most. We find leading whitespace helps models avoid structural deficiencies in subword token representations. We finally present best practices for researchers using language models as zero-shot classifiers with structured output.

Lost in Space: Finding the Right Tokens for Structured Output

As large language models (LLMs) continue to advance, reliable evaluation methods are essential—particularly for open-ended, instruction-following tasks. LLM-as-a-Judge enables automatic evaluation using LLMs as evaluators, but its reliability remains uncertain. In this work, we analyze key factors affecting its trustworthiness, focusing on alignment with human judgments and evaluation consistency. Using BIGGENBench and EvalBiasBench, we study the effects of evaluation design, decoding strategies, and Chain-of-Thought (CoT) reasoning in evaluation. Our results show that evaluation criteria are critical for reliability, non-deterministic sampling improves alignment with human preferences over deterministic evaluation, and CoT reasoning offers minimal gains when clear evaluation criteria are present.

An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability

In many human-annotated NLP tasks involving ambiguity or subjective judgment, annotator disagreement reflects epistemic uncertainty rather than noise. Soft labeling (SL), which represents annotations as probability distributions rather than majority-vote (MV) labels, preserves this uncertainty and can improve downstream performance. We extend this perspective to LLM-based annotation by formalizing LLM soft labeling as introducing controlled variation in model-generated annotations to approximate the latent variability underlying human annotations. We distinguish two sources of variation: model-induced (e.g., stochastic decoding and model ensembles) and human-approximated (e.g., persona prompting and human-calibrated in-context annotation). Using the Gab Hate and GoEmotions datasets, we show that SL training consistently outperforms MV training under stronger LLM-based annotation strategies. Model ensembles produce the most informative soft-label distributions, achieving the best human–LLM agreement and downstream classification performance. These findings suggest that scalable LLM-based annotation pipelines can model epistemic uncertainty through diverse model-level variation without explicitly simulating human attributes.

Capturing Epistemic Uncertainty in LLM-Based Soft Labeling

Most current large language models (LLMs) support a wide variety of languages in addition to English, including high-resource languages (e.g. German, Chinese, French), as well as low-resource ones (e.g. Swahili, Telugu). In addition they have shown impressive capabilities in different domains, like coding, science and math. In this paper, taking math as an example domain, we study the performance of different LLMs across languages. Experimental results show that there exists a non-negligible and consistent gap in the performance of the models across languages. Interestingly, and somewhat against expectations, the gap exists for both high- and low-resource languages. These results should impact further research into cross-lingual capability generalization for next generation LLMs. Or they would, if it weren't for the fact that they are false. By analyzing one of the standard multilingual math benchmarks (MGSM), we determine that several translation errors are present in the data. Furthermore, the lack of standardized answer extraction from LLM outputs further influences the final results. We propose a method for semi-automatic quality assurance to address the first issue at scale, and give recommendations to address the second one. Combining these two approaches we show that the aforementioned language gap mostly disappears, leading to completely different conclusions from our research. We additionally release the corrected dataset to the community.

Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results

Multi-constraint instruction following requires verifying whether a response satisfies multiple individual requirements, yet LLM judges are often assessed only through overall-response judgments. We introduce MCJudgeBench, a benchmark for constraint-level judge evaluation in multi-constraint instruction following. Each instance includes an instruction, a candidate response, an explicit constraint list, per-constraint gold labels in {yes, partial, no}, and controlled response-side perturbations. The evaluation protocol further includes evaluation prompt variants to test judge stability. We evaluate proprietary and open-source LLM judges using both correctness and inconsistency metrics, distinguishing intrinsic inconsistency under stochastic decoding from procedural inconsistency under prompt and response perturbations. Our results show that judge reliability has multiple dimensions: strong overall performance does not guarantee equally reliable detection across label categories, especially for rarer partial and no cases. Judges with higher correctness do not always have lower inconsistency. Evaluation with reasoning improves correctness but does not uniformly improve stability. These findings motivate evaluating LLM judges at the constraint level to study these failure modes.

MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following

Most medical benchmarks for large language models test factual recall through multiple-choice questions, but on-the-ground physicians do not have the luxury of four options to choose from. NOHARM (Wu et al., 2025) demonstrated this limitation using 100 real eConsult cases annotated by 29 board-certified physicians, showing that action-level evaluation reveals omission and commission failure modes invisible to multiple-choice tests. However, NOHARM’s cases are closed and their creation required substantial expert physician time, creating a human bottleneck that limits the scalability and openness of this evaluation approach. We present MedAct, an open replication of NOHARM’s evaluation methodology using synthetically generated cases. Our contribution is a multi-stage generation pipeline that uses language models grounded in clinical practice guidelines to produce 100 cases across ten specialties, each containing roughly 50 plausible next-step actions labeled as Appropriate or Inappropriate using NOHARM’s scoring framework. The pipeline includes structural quality controls: 83 of 100 cases pass all five automated checks, and answer-leaking language appears in only 0.06% of actions. In a pilot evaluation of nine contemporary LLMs using this synthetic benchmark, we observe patterns consistent with NOHARM’s findings on human-curated cases, including that omissions dominate error volume while commissions dominate severe errors. We release all cases, rubrics, generation tooling, and scoring code openly, removing the human-bottleneck barrier to action-level clinical LLM evaluation.

MedAct: Removing the Human Bottleneck in Benchmarking Clinical LLM Safety

Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation.

LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

We present NanoFlux, a novel adversarial framework for generating targeted training data to improve LLM reasoning, where adversarially-generated datasets of $\leq 200$ examples outperform conventional fine-tuning approaches. The framework employs a competitive dynamic between models alternating as Attacker and Defender, supervised by a tool-augmented Judge, synthesizing multi-step questions with explanatory annotations. Fine-tuning a 4B-parameter model on NanoFlux-generated data yields performance gains across diverse domains compared to full-benchmark fine-tuning: +5.9\% on mathematical reasoning, +3.6\% on scientific reasoning, and +16.6\% on medical reasoning, while reducing computational requirements by 3-14×. Ablation studies reveal a non-monotonic relationship between dataset characteristics and model performance, uncovering domain-specific optimal points for question complexity and reasoning quality. NanoFlux automates training data generation through embedding-based novelty filtering, tool-augmented evaluation, and multi-hop reasoning, pointing to the value of small, targeted training datasets.

NanoFlux: Adversarial Dual-LLM Evaluation and Distillation for Multi-Domain Reasoning

We provide a comprehensive review of the FRUIT (Faithfully Reflecting Updated Information in Text) task, which formalizes the challenge of accurately updating textual information with large language models (LLMs). Our work begins with an in-depth analysis of the FRUIT dataset, revealing key structural insights. We also investigate the unsupervised capabilities of LLMs—such as zero-shot learning, chain-of-thought reasoning, self-reflection, and evidence ordering. Experimental results demonstrate that unsupervised approaches perform competitively with supervised methods in faithful text updating. Qualitative analysis shows that updates utilizing table-structured evidence outperform those based on unstructured text. We also discuss important limitations, including the need for new datasets and the risks of information leakage in this domain. These findings have significant implications for applications requiring precise document updates, such as software engineering, technical documentation, and legal document maintenance.

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