All papers

This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives.

This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment.

This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models.

This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar.

This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols.

This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models.

This paper examines neuron relevance characterization methods based on magnitude and distribution support for selecting instruction fine-tuning data.

This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models.

This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations.

This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability.

This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models.

This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps.

This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers.

This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses.

This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages.

This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms.

This paper constructs joint token probability laws from physics models to improve speculative verification in parallel language model decoding.

This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning.

This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations.

This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification.

This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence.

This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions.

This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces.

This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support.

This paper studies cross-entropy surprisal metrics, showing that predictability rankings of texts remain consistent across multiple small language models.

This paper introduces a benchmark and rule-based metric for generating and evaluating audio descriptions in university slide lectures.

This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions.

This paper presents a universal calibrator to improve confidence estimates of in-context classification without requiring model-specific fine-tuning.

This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models.

The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference.

The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints.

The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences.

The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition.

The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence.

The paper introduces a lightweight guidance model to estimate parallel decoding inconsistency and accelerate diffusion language models.

The paper evaluates five language models on extracting complete interval networks of relative temporal events from narratives.

The paper proposes a knowledge distillation method that transfers text language model capabilities to speech-to-speech models using internal text interfaces.

The paper trains language models to report internal states through supervised fine-tuning and on-policy learning.

The paper investigates lexical representations in transformer models by decomposing representations into item, class, and interaction components.

The paper introduces an annotated dataset and retrieval approach for Tagalog homograph diacritic restoration.

The paper studies long chain-of-thought distillation and argues improvements stem from structured reasoning patterns rather than factual correctness.

The paper proposes disaggregating teacher and student models across separate nodes with a fused loss to accelerate knowledge distillation.

The paper proposes an unlearning method that removes sensitive facts from model responses while preserving surrounding non-sensitive factual context.

The paper compares discriminative and generative language models for rhetorical structure parsing and elementary discourse unit segmentation.

The paper repairs multimodal chain-of-thought final answers using original image features without altering the generated reasoning trace.

The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval.

The paper investigates methods for encoding context into model weights so that language models properly apply the temporary context during subsequent inference.

The paper evaluates how language models correct user misconceptions across five languages, showing indirect pushback often fails to fix factual errors.

The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities.

The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction.

The paper tracks automatic speech recognition performance from 2021 to 2025, finding persistent accuracy disparities between standard American English and minority accents.

The paper compares direct prompting, model-distilled supervision, and larger reader datasets for predicting user text highlighting behavior on web pages.

The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance.

The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts.

The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance.

The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion.

The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech.

The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing.

The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing.

The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages.