Summary of The Two Sides Of the Coin: Hallucination Generation and Detection with Llms As Evaluators For Llms, by Anh Thu Maria Bui et al.
The Two Sides of the Coin: Hallucination Generation and Detection with LLMs as Evaluators for LLMs
by Anh Thu Maria Bui, Saskia Felizitas Brech, Natalie Hußfeldt, Tobias Jennert, Melanie Ullrich, Timo Breuer, Narjes Nikzad Khasmakhi, Philipp Schaer
First submitted to arxiv on: 12 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 The paper explores the capabilities of large language models (LLMs) in detecting hallucinated content, a crucial aspect of ensuring their reliability. To achieve this, the authors participated in the CLEF ELOQUENT HalluciGen shared task, developing evaluators for both generating and detecting hallucinated text. Four LLMs, including Llama 3, Gemma, GPT-3.5 Turbo, and GPT-4, were evaluated for their strengths and weaknesses in handling hallucination generation and detection tasks. Ensemble majority voting was employed to combine the models’ outputs for improved detection performance. The results provide valuable insights into the capabilities of these LLMs in this critical area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are used to generate text, but sometimes they make things up that aren’t true! To help us trust what they say, we need ways to detect when they’re making things up. This paper is about how well different LLMs can do this job. The authors tested four big language models: Llama 3, Gemma, GPT-3.5 Turbo, and GPT-4. They also combined the results of all four models to see if it made them better at detecting fake text. This helps us understand how well these models can keep themselves honest. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Llama
