Papers with real-world grounding
From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
| Approach: | This tutorial will review cutting-edge research in MLLMs and examine the impact of ML in learning and reasoning. |
| Outcome: | This course will review cutting-edge research in MLLMs and examine the impact of ML models on learning, learning, and multimodal reasoning. |
Synthetic Data for Evaluation: Supporting LLM-as-a-Judge Workflows with EvalAssist (2025.emnlp-demos)
Copied to clipboard
Martín Santillán Cooper, Zahra Ashktorab, Hyo Jin Do, Erik Miehling, Werner Geyer, Jasmina Gajcin, Elizabeth M. Daly, Qian Pan, Michael Desmond
| Challenge: | EvalAssist is a web-based application designed to assist human-centered evaluation of language model outputs. |
| Approach: | They propose a synthetic data generation tool integrated into EvalAssist to assist human-centered evaluation of language model outputs. |
| Outcome: | The proposed tool supports flexible prompting, RAG-based grounding, persona diversity, and iterative generation workflows. |