2 citations · 8 across the 33 of their papers we have counts for
12 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…
Verification-Aware Planning for Multi-Agent Systems
Tianyang Xu, Dan Zhang, Kushan Mitra +1
Large language model (LLM) agents are increasingly deployed to tackle complex tasks, often necessitating collaboration among multiple specialized agents. However, multi-agent colla…
Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation
Jackson Hassell, Dan Zhang, Hannah Kim +2
We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without parameter updates. While conventi…
AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems
Hannah Kim, Kushan Mitra, Chen Shen +2
Large language models (LLMs) are being increasingly used for planning in orchestrated multi-agent systems. However, existing LLM-based approaches often fall short of human expectat…
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,…
RECAP: REwriting Conversations for Intent Understanding in Agentic Planning
Kushan Mitra, Dan Zhang, Hannah Kim +1
Understanding user intent is essential for effective planning in conversational assistants, particularly those powered by large language models (LLMs) coordinating multiple agents.…
Towards Probabilistic Question Answering Over Tabular Data
Chen Shen, Sajjadur Rahman, Estevam Hruschka
Progress in question answering (QA) over tabular data has enabled reliable factual retrieval from relational tables. However, many real-world questions are probabilistic, requiring…
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…
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
Eser Kandogan, Nikita Bhutani, Dan Zhang +3
Large language models (LLMs) have gained significant interest in industry due to their impressive capabilities across a wide range of tasks. However, the widespread adoption of LLM…
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…