Summary of Anah-v2: Scaling Analytical Hallucination Annotation Of Large Language Models, by Yuzhe Gu et al.
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
by Yuzhe Gu, Ziwei Ji, Wenwei Zhang, Chengqi Lyu, Dahua Lin, Kai Chen
First submitted to arxiv on: 5 Jul 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 A novel iterative self-training framework is introduced to facilitate scalable oversight of large language model (LLM) hallucinations in long-form question-answering tasks. The framework simultaneously scales up the hallucination annotation dataset and improves the accuracy of the annotator using the Expectation Maximization algorithm. Each iteration applies a hallucination annotation pipeline, trains a more accurate annotator, and adopts it for the next iteration. Experimental results show that the finally obtained annotator surpasses GPT-4’s performance and achieves state-of-the-art hallucination detection on HaluEval and HalluQA with zero-shot inference. This annotator can evaluate LLMs’ hallucination levels on a large-scale dataset and aid in mitigating hallucinations in generations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers understand when they make mistakes is developed. Large language models, which are like super smart AI assistants, sometimes make up answers that aren’t true. To fix this problem, researchers created a system that can learn how to spot these fake answers and get better at it over time. The system uses an algorithm called Expectation Maximization to train itself on a large dataset of labeled examples. After testing the system, scientists found that it outperformed other language models, including GPT-4, in detecting fake answers. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Inference » Large language model » Question answering » Self training » Zero shot
