Summary of Iter-ahmcl: Alleviate Hallucination For Large Language Model Via Iterative Model-level Contrastive Learning, by Huiwen Wu et al.
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
by Huiwen Wu, Xiaohan Li, Xiaogang Xu, Jiafei Wu, Deyi Zhang, Zhe Liu
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the significant challenge of hallucination in Large Language Models (LLMs) by introducing a novel approach called Iterative Model-level Contrastive Learning (Iter-AHMCL). The method modifies pre-trained LLM representation layers using contrastive positive and negative models trained on data with and without hallucinations. By leveraging differences between these two models, the approach creates a straightforward pathway to eliminate hallucinations. Experimental validation shows that Iter-AHMCL achieves an average improvement of 10.1 points on the TruthfulQA benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make Large Language Models (LLMs) better by reducing “hallucination” – when they make things up! To do this, it introduces a new way to train these models using something called Iterative Model-level Contrastive Learning (Iter-AHMCL). It’s like teaching the model to correct itself. The results show that this method works well and can even improve the accuracy of what the model says. |
Keywords
» Artificial intelligence » Hallucination
