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Works26 from public data
- Recent advances in deep learning based dialogue systems: a systematic survey360
This survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques.
- OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models232
This investigation confirms that MoE-based LLMs can offer a more favorable cost-effectiveness trade-off than dense LLMs, highlighting the potential effectiveness for future LLM development and proposes potential strategies for mitigating the issues found and further improving off-the-shelf MoE LLM designs.
- Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents66
A new dataset, based on the popular TOD dataset MultiWOZ, is built, by rewriting the existing TOD turns and adding new ODD turns, and it features inter-mode contextual dependency, i.e., the dialogue turns from the two modes depend on each other.
- Diffusion Language Models are Super Data Learners54
A Crossover: when unique data is limited, diffusion language models (DLMs) consistently surpass autoregressive (AR) models by training for more epochs by attributing the gains to three compounding factors: any-order modeling, super-dense compute from iterative bidirectional denoising, and built-in Monte Carlo augmentation.
- A survey on semantic processing techniques54
This survey analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection, to compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.
- Logical Reasoning over Natural Language as Knowledge Representation: A Survey41
This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning.
- MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use40
A comprehensive evaluation of cutting-edge LLMs using a minimal agent framework that operates in a tool-calling loop, significantly surpassing those in previous MCP benchmarks and highlighting the stress-testing nature of MCPMark.
- HiTKG: Towards Goal-Oriented Conversations via Multi-Hierarchy Learning35
This work presents HiTKG, a hierarchical transformer-based graph walker that leverages multiscale inputs to make precise and flexible predictions on KG paths and proposes MetaPath as the backbone method for KG path representation to exploit the entity and relation information concurrently.
- An Embarrassingly Simple Model for Dialogue Relation Extraction32
A simple yet effective model named SimpleRE is proposed for the RE task, which captures the interrelations among multiple relations in a dialogue through a novel input format named BERT Relation Token Sequence.
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- De’hubert: Disentangling Noise in a Self-Supervised Model for Robust Speech Recognition22
A novel training framework, called deHuBERT, is proposed for noise reduction encoding inspired by H. Barlow’s redundancy-reduction principle, which improves the HuBERT training algorithm by introducing auxiliary losses that drive the self- and cross-correlation matrix between pairwise noise-distorted embeddings towards identity matrix.
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- Unnatural Languages Are Not Bugs but Features for LLMs8
This work demonstrates that unnatural languages - strings that appear incomprehensible to humans but maintain semantic meanings for LLMs - contain latent features usable by models, and demonstrates that models fine-tuned on unnatural versions of instruction datasets perform on-par with those trained on natural language.
- MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures6
This work introduces MixEval-X, the first any-to-any, real-world benchmark designed to optimize and standardize evaluations across diverse input and output modalities, and proposes multi-modal benchmark mixture and adaptation-rectification pipelines to reconstruct real-world task distributions.
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- Adaptive Knowledge Distillation Between Text and Speech Pre-Trained Models6
This paper proposes the Prior-informed Adaptive knowledge Distillation (PAD) that adaptively leverages text/speech units of variable granularity and prior distributions to achieve better global and local alignments between text and speech pre-trained models.
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- Boosting LLM via Learning from Data Iteratively and Selectively1
This work proposes to perform instruction tuning by iterative data selection by iteratively updating the complexity score for the top-ranked samples and greedily selecting the ones with the highest complexity-diversity score.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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