6 citations · 10 across the 9 of their papers we have counts for
10 papers
On the Reliability of Networks of AI Agents: Density Evolution, Stopping Sets, and Architecture Optimization
Ehsan Aghazadeh, Hossein Pishro-Nik
Modern AI systems increasingly solve a task not with a single model call but with several imperfect agents working together: some propose pieces of a solution, others verify them,…
Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching
Ehsan Aghazadeh, Masoud Malekzadeh, Ahmad Ghasemi +1
Designing continuous trajectories whose time-averaged occupancy provably matches a prescribed spatial density (the \emph{ergodic coverage} problem) is central to UAV-assisted data…
CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency
Ehsan Aghazadeh, Ahmad Ghasemi, Hedyeh Beyhaghi +1
Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency (Wang et al., 2023)…
A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
Liqiang Jing, Guiming Hardy Chen, Ehsan Aghazadeh +2
Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in multimodal tasks, but visual object hallucination remains a persistent issue. It refers to scenarios whe…
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis
Reshmi Ghosh, Rahul Seetharaman, Hitesh Wadhwa +6
Retrieval Augmented Generation (RAG) is a widely used approach for leveraging external context in several natural language applications such as question answering and information r…
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Hitesh Wadhwa, Rahul Seetharaman, Somyaa Aggarwal +6
Retrieval Augmented Generation (RAG) enriches the ability of language models to reason using external context to augment responses for a given user prompt. This approach has risen…
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition
Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh +2
An emerging solution for explaining Transformer-based models is to use vector-based analysis on how the representations are formed. However, providing a faithful vector-based expla…
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
Mohsen Fayyaz, Ehsan Aghazadeh, Ali Modarressi +3
Current pre-trained language models rely on large datasets for achieving state-of-the-art performance. However, past research has shown that not all examples in a dataset are equal…
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages
Ehsan Aghazadeh, Mohsen Fayyaz, Yadollah Yaghoobzadeh
Human languages are full of metaphorical expressions. Metaphors help people understand the world by connecting new concepts and domains to more familiar ones. Large pre-trained lan…
Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids' Representations
Mohsen Fayyaz, Ehsan Aghazadeh, Ali Modarressi +2
Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models. In this work, we extend the p…