1 citations · 1 across the 4 of their papers we have counts for
5 papers
MAWILE: Multi-Axis Workbench for Inspecting LLM Evaluators
Jackson Hassell, Farima Fatahi Bayat, Pouya Pezeshkpour +1
Large language model (LLM) judges provide a flexible and scalable method for evaluating model and agent outputs, but their verdicts can be sensitive to incidental changes in the ev…
ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs
Jackson Hassell, Chen Shen, Estevam Hruschka
Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consistent evidence conditioning, dep…
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani +17
NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users expre…
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…
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,…