Going beyond the prediction of numerical scores, recent research in automated essay scoring has increasingly emphasized the generation of high-quality feedback that provides justification and actionable guidance. To mitigate the high cost of expert annotation, prior work has commonly relied on LLM-generated feedback to train essay assessment models. However, such feedback is often incorporated without explicit quality validation, resulting in the propagation of noise in downstream applications. To address this limitation, we propose FeedEval, an LLM-based framework for evaluating LLM-generated essay feedback along three pedagogically grounded dimensions: specificity, helpfulness, and validity. FeedEval employs dimension-specialized LLM evaluators trained on datasets curated in this study to assess multiple feedback candidates and select high-quality feedback for downstream use. Experiments on the ASAP++ benchmark show that FeedEval closely aligns with human expert judgments and that essay scoring models trained with FeedEval-filtered high-quality feedback achieve superior scoring performance. Furthermore, revision experiments using small LLMs show that the high-quality feedback identified by FeedEval leads to more effective essay revisions. We release our code and curated datasets at: https://github.com/BBeeChu/FeedEval.git.

Large Language Models (LLMs) have shown remarkable potential in recommendation systems but suffer from prohibitive inference latency. Existing distillation approaches typically target Small Language Models (SLMs) or Conventional Recommendation Models (CRMs), face a critical trade-off between computational cost and semantic reasoning capacity. To bridge this accuracy-efficiency gap, we introduce Reasoning-to-Encoder Distillation (R2END), a framework that establishes a text encoder as the optimal student architecture for scalable recommendation. Unlike methods that mimic token generation, R2END compresses the teacher’s reasoning into a dense vector space via a semantic alignment objective, effectively capturing user-item dynamics. Extensive experiments on four datasets demonstrate that R2END not only outperforms state-of-the-art baselines but also achieves drastically reduced latency, offering a sweet spot for recommendation.

Eliciting high-quality reasoning trajectories to supervise Large Language Models (LLMs) in sequential recommendation is notoriously difficult. In massive item pools, standard data curation methods such as autonomous exploration or rejection sampling suffer from severe sparsity. LLMs’ context-utilization bias exacerbate this challenge by overusing noisy interaction histories, yielding diffuse and weakly grounded reasoning paths that frequently fail to retrieve the target item. To overcome this data curation bottleneck, we propose GEARec (Grounded Reasoning Curation for Anchor-based Recommendation), a framework based on Dual-Perspective Anchor Extraction and Hybrid Reasoning Generation. By combining the semantic strengths of LLMs with collaborative filtering (CF) signals, GEARec identifies a high-relevance anchor item from the user’s history to construct highly focused, reachability-aware reasoning trajectories for supervised fine-tuning. Experiments on multiple datasets demonstrate that GEARec effectively curates superior supervision signals, reducing context overuse and consistently outperforming strong baselines in recommendation accuracy.

With recent advances in diffusion models, text-to-image (T2I) generation has achieved remarkable progress, producing high-quality images from simple text prompts. However, as these models have become more expressive, concerns regarding the generation of unsafe or inappropriate content have grown. To address this issue, we propose the Safety-Adjustable Framework for Ethics (Safe), a novel training-free, plug-and-play prompt optimization framework. Leveraging the reasoning capabilities of large language models (LLMs), Safe optimizes prompts to meet concept-specific safety requirements while preserving their original semantics. The framework operates in three stages: (1) generating an image from the original prompt using a pretrained T2I model, (2) analyzing the generated image with a vision-language model (VLM), and (3) conducting DAG-based reasoning over both the prompt and the image description to produce refined prompt. In addition, Safe uses a search module to incorporate external knowledge such as cultural norms and legal constraints, enabling adaptation to diverse user contexts. Extensive experiments demonstrate that Safe effectively complements existing safety methods and achieves strong performance in user-centric safety alignment.

Documents that users query mix tables with prose, but the QA systems built for them do not: retrieve-and-read pipelines flatten tables, while text-to-SQL pipelines ignore passages. Corporate filings, scientific reports, and Wikipedia articles all sit on this fault line, and a single user question routinely crosses it. We present ATR (AgenticTableRAG), an agentic framework for hybrid table-text question answering. ATR decomposes each question into ordered sub-queries and dispatches each to one of four execution primitives: text reading, cell retrieval, SQL execution, or a hybrid of cell grounding and SQL. When the retrieved evidence does not support the predicted answer, the router is re-invoked with the failed-route history and dispatches a different primitive. Value grounding composed with constrained SQL execution rules out column and value hallucinations by construction. The routing policy is distilled into a lightweight student model that transfers across benchmarks without additional inference-time LLM calls. A single ATR configuration leads on token F1 across three benchmarks without per-dataset tuning.

Existing automated essay scoring (AES) has solely relied on essay text without using explanatory rationales for the scores, thereby forgoing an opportunity to capture the specific aspects evaluated by rubric indicators in a fine-grained manner. This paper introduces Rationale-based Multiple Trait Scoring (RMTS), a novel approach for multi-trait essay scoring that integrates prompt-engineering-based large language models (LLMs) with a fine-tuning-based essay scoring model using a smaller large language model (S-LLM). RMTS uses an LLM-based trait-wise rationale generation system where a separate LLM agent generates trait-specific rationales based on rubric guidelines, which the scoring model uses to accurately predict multi-trait scores. Extensive experiments on benchmark datasets, including ASAP, ASAP++, and Feedback Prize, show that RMTS significantly outperforms state-of-the-art models and vanilla S-LLMs in trait-specific scoring. By assisting quantitative assessment with fine-grained qualitative rationales, RMTS enhances the trait-wise reliability, providing partial explanations about essays. The code is available at https://github.com/BBeeChu/RMTS.git.

Large Language Models (LLMs) have recently emerged as promising tools for knowledge tracing due to their strong reasoning and generalization abilities. While recent LLM-based KT methods have introduced new prompt formats, they struggle to reflect the histories of example learners within a single prompt during in-context learning (ICL), leading to limited scalability and high computational cost under token constraints. In this work, we present LLM-based Option weighted Knowledge Tracing (LOKT), a simple yet effective LLM-based knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights (TCOW). These are semantic labels (e.g., “inadequate”) assigned to the options selected by learners when answering questions helping understand LLM. Experiments on multiple-choice datasets show that LOKT outperforms existing LLM-based KT models in both warm-start and few-shot settings. Moreover, LOKT enables scalable and cost-efficient inference, performing strongly even under strict token constraints. Our code is available at https://anonymous.4open.science/r/LOKT_model-3233

Recent studies have explored integrating large langucage models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture.To effectively address these issues, we propose a Query-to-Recommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the other. In this framework, LLMs are utilized to generate feature-enriched item descriptions and personalized user queries, allowing for capturing diverse preferences and enabling rich semantic matching in a zero-shot manner. To effectively combine the complementary strengths of LLM and collaborative signals, we introduce an adaptive reranking strategy. Extensive experiments demonstrate an improvement in performance up to 57%, while also improving the novelty and diversity of recommendations.

Recent advancements in large language models (LLMs) have boosted research on generating SQL queries from domain-specific questions, particularly in the medical domain. A key challenge is detecting and filtering unanswerable questions. Existing methods often relying on model uncertainty, but these require extra resources and lack interpretability. We propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. Furthermore, we found that LLM-generated schema descriptions can significantly enhance the prediction accuracy. Our method provides a resource-efficient solution for unanswerable question detection in domain-specific question answering systems.