565 citations · 621 across the 33 of their papers we have counts for
6 papers · 1 filter
From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models
Farima Fatahi Bayat, Pouya Pezeshkpour, Estevam Hruschka
Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains re…
Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling
Seiji Maekawa, Jackson Hassell, Pouya Pezeshkpour +2
Existing benchmarks for tool-augmented language models (TaLMs) lack fine-grained control over task difficulty and remain vulnerable to data contamination. We present FuncBenchGen,…
Mixed Signals: Decoding VLMs' Reasoning and Underlying Bias in Vision-Language Conflict
Pouya Pezeshkpour, Moin Aminnaseri, Estevam Hruschka
Vision-language models (VLMs) have demonstrated impressive performance by effectively integrating visual and textual information to solve complex tasks. However, it is not clear ho…
Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation
Pouya Pezeshkpour, Estevam Hruschka
Retrieval Augmented Generation (RAG) frameworks have shown significant promise in leveraging external knowledge to enhance the performance of large language models (LLMs). However,…
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education
Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani +1
Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operationa…
Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?
Pouya Pezeshkpour, Estevam Hruschka
Large Language Models (LLMs) have demonstrated remarkable performance on various tasks, yet their ability to extract and internalize deeper insights from domain-specific datasets r…