Papers with model enhancement
Using Interpretation Methods for Model Enhancement (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing frameworks for enhancing neural models with interpretation methods and gold rationales have not been fully explored. |
| Approach: | They propose a framework for utilizing interpretation methods and gold rationales to enhance neural models. |
| Outcome: | The proposed framework outperforms gradient-based methods in low-resource settings on a variety of tasks. |
[MASK] Insertion: a robust method for anti-adversarial attacks (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies have focused on adversarial defenses against pretrained language models. |
| Approach: | They propose an adversarial defensing algorithm that inserts tokens into input sequences . they show an improvement in accuracy between 3.2 and 11.1 absolute points . |
| Outcome: | The proposed algorithm improves model accuracy on clean and polluted inputs compared with state-of-the-art models . |
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models (2024.findings-emnlp)
Copied to clipboard
Bolei Ma, Xinpeng Wang, Tiancheng Hu, Anna-Carolina Haensch, Michael Hedderich, Barbara Plank, Frauke Kreuter
| Challenge: | Recent advances in Large Language Models have sparked interest in validating human-like cognitive-behavioral traits. |
| Approach: | They examine whether LLM outputs reflect human-like cognitive-behavioral traits . they find that measuring AOVs embedded within LLMs remains opaque . |
| Outcome: | The proposed model can be used to evaluate human-like cognitive-behavioral traits . the proposed model could be used in writing assistants and other applications . |
Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models. |
| Approach: | They propose a method to integrate multiple models from diverse training scenarios into a unified model. |
| Outcome: | The proposed method outperforms state-of-the-art models on mainstream language models by large margins. |