Papers with ensembled models
URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation. |
| Approach: | They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks. |
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errors (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for detecting factual errors in text summarization are inadequate for the task. |
| Approach: | They propose an end-to-end large language model framework for detecting factual errors in text summarization. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) balanced accuracy on the AggreFact-XSUM FTSOTA, TofuEval Summary-Level, and HaluEVAL Summarization benchmarks. |