4 papers
How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling
Dhananjay Ashok, Shantanu Agarwal, Vivek Datla +2
World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Langu…
VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models
Zhiqi Huang, Vivek Datla, Zhichao Xu +3
Neural ranking models have become core components of modern information retrieval systems and important building blocks of AI systems such as retrieval-augmented generation (RAG) p…
Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment
Mengxuan Hu, Vivek V. Datla, Anoop Kumar +4
Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have impro…
Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation
Zhiqi Huang, Vivek Datla, Chenyang Zhu +4
We propose a method for confidence estimation in retrieval-augmented generation (RAG) systems that aligns closely with the correctness of large language model (LLM) outputs. Confid…