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20212026
most citedAdvanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection

27 citations · 48 across the 44 of their papers we have counts for

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Showing 2025 · cs.CLShow all

6 papers · 2 filters

cs.CL2025

Audit-of-Understanding: Posterior-Constrained Inference for Mathematical Reasoning in Language Models

Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe +1

Large language models (LLMs) often generate reasoning traces that appear coherent but rest on unsupported assumptions, leading to hallucinated conclusions. Prior work mainly addres…

cs.CL2025

Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference

Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe +1

Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplor…

cs.CL2025

Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models

Samir Abdaljalil, Hasan Kurban, Khalid Qaraqe +1

Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret. Prompti…

cs.CL2025★ 2 cited

HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinations

Samir Abdaljalil, Hasan Kurban, Erchin Serpedin

Large Language Models (LLMs) are increasingly used in various contexts, yet remain prone to generating non-factual content, commonly referred to as "hallucinations". The literature…

cs.CL2025

SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs

Samir Abdaljalil, Hasan Kurban, Parichit Sharma +2

Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs - commonly known as "hallucinations." Am…

cs.CL2025

SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs

Samir Abdaljalil, Filippo Pallucchini, Andrea Seveso +3

Despite the state-of-the-art performance of Large Language Models (LLMs), these models often suffer from hallucinations, which can undermine their performance in critical applicati…