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Tutorial: Exploring SHAP-IQ Visualizations
In this tutorial, we’ll explore a range of SHAP-IQ visualizations that provide insights into how...
Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions
arXiv:2501.01872v5 Announce Type: replace Abstract: Large language models, despite extensive alignment with human values and...
TurkBench: A Benchmark for Evaluating Turkish Large Language Models
arXiv:2601.07020v2 Announce Type: replace Abstract: With the recent surge in the development of large language...
TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers
arXiv:2505.08402v1 Announce Type: new Abstract: Recently, large language models(LLMs) have played an increasingly important role...
TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs
arXiv:2506.23423v1 Announce Type: new Abstract: Past work has studied the effects of fine-tuning on large...
TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings
arXiv:2603.04772v1 Announce Type: new Abstract: Despite the exceptional reasoning capabilities of Multimodal Large Language Models...
Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images
arXiv:2601.12960v1 Announce Type: new Abstract: Integrating deep learning into healthcare enables personalized care but raises...
Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal
arXiv:2509.01455v1 Announce Type: new Abstract: Deployed language models must decide not only what to answer...
Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning
arXiv:2602.23440v3 Announce Type: replace Abstract: Reinforcement learning has emerged as an effective paradigm for training...
Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models
arXiv:2505.17826v2 Announce Type: replace-cross Abstract: Trinity-RFT is a general-purpose, unified and easy-to-use framework designed for...
TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
arXiv:2510.07118v2 Announce Type: replace Abstract: Instruction tuning is essential for aligning large language models (LLMs)...
Treating enterprise AI as an operating layer
There’s a fault line running through enterprise AI, and it’s not the one getting the...
