111 citations · 282 across the 52 of their papers we have counts for
16 papers · 2 filters
CogMem: A Cognitive Memory Architecture for Sustained Multi-Turn Reasoning in Large Language Models
Yiran Zhang, Jincheng Hu, Mark Dras +1
Large language models (LLMs) excel at single-turn reasoning but often lose accuracy and coherence over extended, multi-turn interactions. Recent evaluations such as TurnBench highl…
Beyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA
Yiran Zhang, Mingyang Lin, Mark Dras +1
Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world…
SHIELD: Classifier-Guided Prompting for Robust and Safer LVLMs
Juan Ren, Mark Dras, Usman Naseem
Large Vision-Language Models (LVLMs) unlock powerful multimodal reasoning but also expand the attack surface, particularly through adversarial inputs that conceal harmful goals in…
Steering Over-refusals Towards Safety in Retrieval Augmented Generation
Utsav Maskey, Mark Dras, Usman Naseem
Safety alignment in large language models (LLMs) induces over-refusals -- where LLMs decline benign requests due to aggressive safety filters. We analyze this phenomenon in retriev…
Fairness Evaluation and Inference Level Mitigation in LLMs
Afrozah Nadeem, Mark Dras, Usman Naseem
Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, a…
Steering Towards Fairness: Mitigating Political Bias in LLMs
Afrozah Nadeem, Mark Dras, Usman Naseem
Recent advancements in large language models (LLMs) have enabled their widespread use across diverse real-world applications. However, concerns remain about their tendency to encod…