Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes.In practice, the interdependence among attributes makes it challenging for language models to satisfy correlated constraints consistently.Moreover, previous approaches often require perattribute fine-tuning, limiting flexibility across diverse summary attributes.In this paper, we propose adaptive planning for multi-attribute controllable summarization (PACO), a trainingfree framework that reframes the task as planning the order of sequential attribute control with a customized Monte Carlo Tree Search (MCTS).In PACO, nodes represent summaries, and actions correspond to single-attribute adjustments, enabling progressive refinement of only the attributes requiring further control.This strategy adaptively discovers optimal control orders, ultimately producing summaries that effectively meet all constraints.Extensive experiments across diverse domains and models demonstrate that PACO achieves robust multi-attribute controllability, surpassing both LLM-based self-planning models and finetuned baselines.Remarkably, PACO with Llama-3.2-1Brivals the controllability of the much larger Llama-3.3-70Bbaselines.With larger models, PACO achieves superior control performance, outperforming all competitors.

• We propose RaDME, a self-explainable multi-trait AES model, achieving four key gains. • Explainability : generate explicit trait-wise rationales for model-generated scores. • Accuracy : rationale generation even improves scoring performance. • Efficiency : distilling LLM reasoning capacity yields a lightweight scorer. • Consistency : reveal that a scoring-first design stabilizes scores and explanations. Multi-trait automated essay scoring (AES) systems provide a fine-grained evaluation of an essay’s diverse aspects. While they excel in scoring, prior systems fail to explain why specific trait scores are assigned. This lack of transparency leaves instructors and learners unconvinced of the AES outputs, hindering their practical use. To address this, we propose a self-explainable Rationale-Driven Multi-trait automated Essay scoring (RaDME) 1 1 Codes and all generated results will be publicly available. framework. RaDME leverages the reasoning capabilities of large language models (LLMs) by distilling them into a smaller yet effective scorer. This more manageable student model is optimized to sequentially generate a trait score followed by the corresponding rationale, thereby inherently learning to select a more justifiable score by considering the subsequent rationale during training. Our findings indicate that while LLMs underperform in direct AES tasks, they excel in rationale generation when provided with precise numerical scores. Thus, RaDME integrates the superior reasoning capacities of LLMs into the robust scoring accuracy of an optimized, smaller model. Extensive experiments demonstrate that RaDME achieves both accurate and adequate reasoning while supporting high-quality multi-trait scoring, significantly enhancing the transparency of AES.

With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attention.However, prior approaches mainly depend on document-level comparisons using basic indicators like citations due to the lack of facet-level relevance datasets; yet, this limits their use to citation-based domains and fails to capture the intricacies of facet constraints.In this paper, we propose a multi-facet blending (FaBle) augmentation method, which exploits modularity by decomposing and recomposing to explicitly synthesize facet-specific training sets.We automatically decompose documents into facet units and generate (ir)relevant pairs by leveraging LLMs' intrinsic distinguishing capabilities; then, dynamically recomposing the units leads to facet-wise relevance-informed document pairs.Our modularization eliminates the need for pre-defined facet knowledge or labels.Further, to prove the FaBle's efficacy in a new domain beyond citation-based scientific paper retrieval, we release a benchmark dataset for educational exam item QBE.FaBle augmentation on 1K documents remarkably assists training in obtaining facet conditional embeddings.

Robust supervised fine-tuned small Language Models (sLMs) often show high reliability but tend to undercorrect.They achieve high precision at the cost of low recall.Conversely, Large Language Models (LLMs) often show the opposite tendency, making excessive overcorrection, leading to low precision.To effectively harness the strengths of LLMs to address the recall challenges in sLMs, we propose Post-Correction via Overcorrection (PoCO), a novel approach that strategically balances recall and precision.PoCO first intentionally triggers overcorrection via LLM to maximize recall by allowing comprehensive revisions, then applies a targeted post-correction step via fine-tuning smaller models to identify and refine erroneous outputs.We aim to harmonize both aspects by leveraging the generative power of LLMs while preserving the reliability of smaller supervised models.Our extensive experiments demonstrate that PoCO effectively balances GEC performance by increasing recall with competitive precision, ultimately improving the overall quality of grammatical error correction.

JinMyeong An, Sangwon Ryu, Heejin Do, Yunsu Kim, Jungseul Ok, Gary Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Subin Kim, Hoonrae Kim, Heejin Do, Gary Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Wonjun Lee, Solee Im, Heejin Do, Yunsu Kim, Jungseul Ok, Gary Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency.However, existing summarization methods often target a specific dimension, facing challenges in generating well-balanced summaries across multiple dimensions.In this paper, we propose multiobjective reinforcement learning tailored to generate balanced summaries across all four dimensions.We introduce two multi-dimensional optimization (MDO) strategies for adaptive learning: 1) MDO min , rewarding the current lowest dimension score, and 2) MDO pro , optimizing multiple dimensions similar to multi-task learning, resolves conflicting gradients across dimensions through gradient projection.Unlike prior ROUGE-based rewards relying on reference summaries, we use a QA-based reward model that aligns with human preferences.Further, we discover the capability to regulate the length of summaries by adjusting the discount factor, seeking the generation of concise yet informative summaries that encapsulate crucial points.Our approach achieved substantial performance gains compared to baseline models on representative summarization datasets, particularly in the overlooked dimensions.

Recent advances in automated essay scoring (AES) have shifted towards evaluating multiple traits to provide enriched feedback.Like typical AES systems, multi-trait AES employs the quadratic weighted kappa (QWK) to measure agreement with human raters, aligning closely with the rating schema; however, its non-differentiable nature prevents its direct use in neural network training.In this paper, we propose Scoring-aware Multi-reward Reinforcement Learning (SaMRL), which integrates actual evaluation schemes into the training process by designing QWK-based rewards with a mean-squared error penalty for multi-trait AES.Existing reinforcement learning (RL) applications in AES are limited to classification models despite associated performance degradation, as RL requires probability distributions; instead, we adopt an autoregressive score generation framework to leverage token generation probabilities for robust multi-trait score predictions.Empirical analyses demonstrate that SaMRL facilitates model training, notably enhancing scoring of previously inferior prompts.

Remarkable advances in large language models (LLMs) have enabled high-quality text summarization.However, this capability is currently accessible only through LLMs of substantial size or proprietary LLMs with usage fees.In response, smallerscale LLMs (sLLMs) of easy accessibility and low costs have been extensively studied, yet they often suffer from missing key information and entities, i.e., low relevance, in particular, when input documents are long.We hence propose a key-elementinformed instruction tuning for summarization, so-called KEIT-Sum, which identifies key elements in documents and instructs sLLM to generate summaries capturing these key elements.Experimental results on dialogue and news datasets demonstrate that sLLM with KEITSum indeed provides high-quality summarization with higher relevance and less hallucinations, competitive to proprietary LLM.

Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However, existing multi-aspect multi-granularity methods simultaneously predict all aspects at all granularity levels; therefore, they have difficulty in capturing the linguistic hierarchy of phoneme, word, and utterance. This limitation further leads to neglecting intimate cross-aspect relations at the same linguistic unit. In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations. By obtaining relational information from both the granularity- and aspect-side, HiPAMA can take full advantage of multi-task learning. Remarkable improvements in the experimental results on the speachocean762 datasets demonstrate the robustness of HiPAMA, particularly in the difficult-to-assess aspects.

With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress.However, despite the highly imbalanced score labels within each aspect, existing studies have rarely tackled the data imbalance problem.In this paper, we suggest a novel loss function, score-balanced loss, to address the problem caused by uneven data, such as bias toward the majority scores.As a re-weighting approach, we assign higher costs when the predicted score is of the minority class, thus, guiding the model to gain positive feedback for sparse score prediction.Specifically, we design two weighting factors by leveraging the concept of an effective number of samples and using the ranks of scores.We evaluate our method on the speechocean762 dataset, which has noticeably imbalanced scores for several aspects.Improved results particularly on such uneven aspects prove the effectiveness of our method.

Multilingual NMT has developed rapidly, but still has performance degradation caused by language diversity and model capacity constraints. To achieve the competitive accuracy of multilingual translation despite such limitations, knowledge distillation, which improves the student network by matching the teacher network’s output, has been applied and shown enhancement by focusing on the important parts of the teacher distribution. However, existing knowledge distillation methods for multilingual NMT rarely consider the knowledge, which has an important function as the student model’s target, in the process. In this article, we propose two distillation strategies that effectively use the knowledge to improve the accuracy of multilingual NMT. First, we introduce a language-family-based approach, guiding to select appropriate knowledge for each language pair. By distilling the knowledge of multilingual teachers that each processes a group of languages classified by language families, the multilingual model overcomes accuracy degradation caused by linguistic diversity. Second, we propose target-oriented knowledge distillation, which intensively focuses on the ground-truth target of knowledge with a penalty strategy. Our method provides a sensible distillation by penalizing samples without actual targets, while additionally targeting the ground-truth targets. Experiments using TED Talk datasets demonstrate the effectiveness of our method with BLEU scores increment. Discussions of distilled knowledge and further observations of the methods also validate our results.