10 citations · 14 across the 11 of their papers we have counts for
12 papers
Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models
Jiawei Xu, Minghui Liu, Aakriti Agrawal +2
Masked diffusion language models decode by iteratively unmasking tokens, where the unmasking order defines an "order of thought" that strongly influences generation quality yet is…
The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning
Aakriti Agrawal, Souradip Chakraborty, Armin Saghafian +6
Process Reward Models (PRMs) improve credit assignment for reasoning by providing step-level feedback. However, we identify a hidden bias in PRMs caused by severe imbalance in step…
VeriGate: Verifier-Gated Step-Level Supervision for GRPO
Aakriti Agrawal, Minghui Liu, Furong Huang
Group Relative Policy Optimization (GRPO) is an effective recipe for training reasoning models with verifier-based outcome rewards, but its supervision is sparse: when all sampled…
Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti +6
Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual information during multimodal reasoning.…
Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems
Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh +3
Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge, particularly in resource-c…
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles
Aakriti Agrawal, Mucong Ding, Zora Che +6
With Large Language Models (LLMs) rapidly approaching and potentially surpassing human-level performance, it has become imperative to develop approaches capable of effectively supe…
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models
Michael-Andrei Panaitescu-Liess, Pankayaraj Pathmanathan, Yigitcan Kaya +5
As the capabilities of large language models (LLMs) continue to expand, their usage has become increasingly prevalent. However, as reflected in numerous ongoing lawsuits regarding…
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles
Aakriti Agrawal, Mucong Ding, Zora Che +6
With Large Language Models (LLMs) rapidly approaching and potentially surpassing human-level performance, it has become imperative to develop approaches capable of effectively supe…
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Mucong Ding, Chenghao Deng, Jocelyn Choo +8
While generalization over tasks from easy to hard is crucial to profile language models (LLMs), the datasets with fine-grained difficulty annotations for each problem across a broa…
WAVES: Benchmarking the Robustness of Image Watermarks
Bang An, Mucong Ding, Tahseen Rabbani +8
In the burgeoning age of generative AI, watermarks act as identifiers of provenance and artificial content. We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a ben…
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning
Aakriti Agrawal, Rohith Aralikatti, Yanchao Sun +1
Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-worl…
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
J. K. Terry, Nathaniel Grammel, Sanghyun Son +2
Parameter sharing, where each agent independently learns a policy with fully shared parameters between all policies, is a popular baseline method for multi-agent deep reinforcement…