14 citations · 24 across the 11 of their papers we have counts for
13 papers
Mo' Models, Mo' Problems: How to best select model pools when designing Multi-Agent Systems
Sara Vera Marjanović, Jiacheng Xu, Aleksandr Laptev +3
Multi-agent Systems (MAS) combine multiple model outputs to solve complex reasoning tasks. However, despite rapid growth of available open-source models, there is limited research…
From Zero to Hero: An Open LLM Ecosystem for Armenian
Erik Arakelyan, Khatun Avetisyan, Meri Davtyan +5
Pretraining data for Armenian, a morphologically rich and low-resource language, is scarce, and no open Armenian LLM has been released with the data and recipe needed to reproduce…
More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs
Marina Igitkhanian, Erik Arakelyan
Recently, language models have made rapid progress across various domains and applications. However, their capability for self-improvement, i.e., whether they are adept at recognis…
SHERLOC: Structured Diagnostic Localization for Code Repair Agents
Hovhannes Tamoyan, Sean Narenthiran, Erik Arakelyan +2
LLM agents solve repository-level coding tasks through multi-turn tool use, but utilize half their budget on locating faults before editing. Dedicated localization frameworks have…
With Great Backbones Comes Great Adversarial Transferability
Erik Arakelyan, Karen Hambardzumyan, Davit Papikyan +4
Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet}…
FLARE: Faithful Logic-Aided Reasoning and Exploration
Erik Arakelyan, Pasquale Minervini, Pat Verga +2
Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the result…
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages
Gayane Ghazaryan, Erik Arakelyan, Pasquale Minervini +1
Question Answering (QA) datasets have been instrumental in developing and evaluating Large Language Model (LLM) capabilities. However, such datasets are scarce for languages other…
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
Erik Arakelyan, Zhaoqi Liu, Isabelle Augenstein
Recent studies of the emergent capabilities of transformer-based Natural Language Understanding (NLU) models have indicated that they have an understanding of lexical and compositi…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
Erik Arakelyan, Arnav Arora, Isabelle Augenstein
Stance Detection is concerned with identifying the attitudes expressed by an author towards a target of interest. This task spans a variety of domains ranging from social media opi…
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Erik Arakelyan, Pasquale Minervini, Daniel Daza +2
Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior wor…
Complex Query Answering with Neural Link Predictors
Erik Arakelyan, Daniel Daza, Pasquale Minervini +1
Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more…
Fluid Dynamics Modeling : The Numerical Solution Of 2D Navier Hyperbolic Equations
Erik Arakelyan, Aram Serobyan, Narek Jilavyan
In the following paper we will consider Navier-Stokes problem and it's interpretation by hyperbolic waves, focusing on wave propagation. We will begin with solution for linear wave…