Summary of Enhancing Hallucination Detection Through Perturbation-based Synthetic Data Generation in System Responses, by Dongxu Zhang et al.
Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses
by Dongxu Zhang, Varun Gangal, Barrett Martin Lattimer, Yi Yang
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Detecting hallucinations in large language model (LLM) outputs is crucial. Traditional fine-tuning for this task is hindered by the costly and outdated annotation process, particularly across various vertical domains and with rapid LLM advancements. This study proposes an approach that automatically generates both faithful and hallucinated outputs by rewriting system responses. Experimental results show a T5-base model, fine-tuned on our generated dataset, outperforms state-of-the-art zero-shot detectors and existing synthetic generation methods in terms of accuracy and latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out when AI chatbots are making things up! It’s important to detect when language models make mistakes. Right now, it takes a lot of time and money to train these models to recognize when they’re making stuff up. This paper introduces a new way to do this by creating fake and real responses from language models. The results show that their approach is better than what others have tried before in terms of accuracy and speed. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » T5 » Zero shot
