Large Language Model
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents Chang Ma
However, the evaluation process presents substantial challenges.
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-shot Forecasting of Multivariate Time Series Vijay Ekambaram IBM Research Bangalore, India
Consequently, recent research efforts have focused on developing pre-trained TS forecasting models.
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
Inspired by our findings, we propose V accine, a perturbation-aware alignment technique to mitigate the security risk of users fine-tuning.
Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees Y u Gui
Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values.
Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
Training (AL T), which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples.
Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
We revisit this scenario as an experimental case study in Section 6.
Order-Independence Without Fine Tuning
Reid McIlroy-Young, Katrina Brown, Conlan Olson, Linjun Zhang, Cynthia Dwork
Unlike humans, these ' Large Language Models ' (LLMs) are highly sensitive to small changes in their inputs, leading to unwanted inconsistency in their behavior.