10 citations · 20 across the 9 of their papers we have counts for
16 papers
Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading
Nicholas Sadjoli, Tim Siefken, Atin Ghosh +2
Current Large Language Model (LLM) evaluation frameworks utilize the same static prompt template across all models under evaluation. This differs from the common industry practice…
Characterizing Delusional Spirals through Human-LLM Chat Logs
Jared Moore, Ashish Mehta, William Agnew +11
As large language models (LLMs) have proliferated, disturbing anecdotal reports of negative psychological effects, such as delusions, self-harm, and ``AI psychosis,'' have emerged…
Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis
Penny Chong, Harshavardhan Abichandani, Jiyuan Shen +4
Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a sca…
OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets
Jiyuan Shen, Peiyue Yuan, Atin Ghosh +2
Multimodal Large Language Models (MLLMs) enhance the potential of natural language processing. However, their actual impact on document information extraction remains unclear. In p…
Structured Prompts Improve Evaluation of Language Models
Asad Aali, Muhammad Ahmed Mohsin, Vasiliki Bikia +15
As language models (LMs) are increasingly adopted across domains, high-quality benchmarking frameworks are essential for guiding deployment decisions. In practice, however, framewo…
Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation
Sayash Kapoor, Benedikt Stroebl, Peter Kirgis +28
AI agents have been developed for complex real-world tasks from coding to customer service. But AI agent evaluations suffer from many challenges that undermine our understanding of…