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This paper presents a method for multilingual grievance labeling using contextual models to improve upon word-level dictionaries, addressing circular evaluation and achieving better performance by reading surrounding context.
This paper explores whether valence features can reflect morality in natural language by analyzing human annotations of moral scenarios, finding significant correlations and achieving a Matthew's correlation coefficient of 0.764 for binary morality classification.
This paper presents a three-stage neural-symbolic pipeline for gaming toxicity detection, combining transformer ensembles with rule-based mediation, achieving top accuracy in the EEUCA 2026 shared task.
This paper describes LLM-INSTRUCT, the winning system for the UZH Shared Task at ArgMining 2026 on paragraph-level argument mining. The system uses constraint-aware retrieval and selective debate to improve accuracy and schema compliance.
This article argues that text-to-SQL benchmarks must account for the complexities and challenges of real-world data stores, not just idealized datasets.
This paper proposes a rationale-guided knowledge distillation framework for cross-lingual stance detection, using chain-of-thought prompting from large language models to train a compact student model with dual-path distillation and contrastive learning.
This paper explores using fine-tuned LLMs to identify indicators of vulnerability (mental ill health, substance misuse, alcohol dependence, homelessness) in UK police incident logs, finding that while LLMs can produce meaningful prevalence estimates, they require careful methodological support and are not reliable for individual-level decisions.
AEGIS is an exploratory framework for studying span-level guided multilingual detoxification across English, Mandarin Chinese, and Korean, analyzing how explicit rationales affect the trade-off between toxicity reduction and meaning preservation.
This paper presents adaptive pipelines for legal retrieval, entailment, and judgment prediction tasks in the COLIEE 2026 competition, using multi-stage retrieval, reranking, and LLM-based reasoning.
This paper introduces MoNIM, a learnable memory module that integrates induction capabilities of attention heads with feed-forward networks to enable scalable continual learning in semiparametric language models, improving efficiency and retention of new knowledge.
The paper proposes converting multimodal patient data (text, labs, vitals) into a single natural language sequence and fine-tuning LLMs for clinical prediction, achieving comparable or better performance than specialized fusion architectures across three tasks.
Keio University has made public lecture slides for Digital Signal Processing and Natural Language Processing, available for anyone to use for learning.
This paper introduces Epistemic Stance Flexibility Probing (ESFP), a behavioral benchmark that measures how large language models shift their epistemic register between attributing claims to experts and expressing their own stance. Evaluating eight frontier models, the authors find that epistemic flexibility is largely orthogonal to general capability, with stance content density providing the strongest signal.
A research paper proposing SeRIn, a multimodal fusion scheme that separates modality-specific refinement from cross-modal interaction, achieving state-of-the-art on CH-SIMS and CMU-MOSEI benchmarks for sentiment analysis.
This paper investigates the feasibility of using translation-based fine-tuning as a resource-efficient alternative to native-language BERT models for low-resource languages, finding it comparable or superior in 53.3% of cases across six NLP tasks.
Pythia is a multi-agent system that autonomously writes and optimizes extraction prompts for clinical concepts without manual prompt engineering or fine-tuning, using a locally hosted open-weights model. It achieves mean sensitivity of 0.76 and specificity of 0.95 on clinical symptom detection, outperforming lexicon-based methods on specificity.
This paper introduces Reference Abstraction (RA), Summary Abstraction (SA), and Abstraction Ratio (AR) metrics to quantify abstractiveness in text summarization, using harmonic mean of document lengths and cubic non-overlap factor. Empirical evaluation on XSUM with four models shows the metrics effectively discriminate between extractive and abstractive summaries, and flag potential hallucination.
This paper proposes a delimiter-guided bottom-up constituency parser that removes the need for explicit arity actions by deriving arity from stack configurations, achieving competitive results on PTB and CTB with a smaller action inventory.
Introduces Depth-Entropy Guided Sampling (DEGS), a training-free test-time method that exploits layer-wise entropy collapse in LLMs to improve reasoning without RL training, achieving competitive results with RL-posttrained models.
The paper proposes CasAug, a relation extraction model based on the CasRel framework with a semantic enhancement mechanism to address the triple overlap problem, showing improved performance over baseline models.