Summary of On a Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation, by Xiaonan Jing et al.
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation
by Xiaonan Jing, Srinivas Billa, Danny Godbout
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 A novel approach to evaluating faithfulness in guided natural language generation (NLG) is presented, tackling the challenge of unfaithful content in real-world applications. The study develops a rubric template and leverages large language models (LLMs) like GPT-4 to score generations on quantifiable scales. Comparisons are made with popular LLMs and natural language inference (NLI) models for quality and sensitivity. Synthetic unfaithful data is generated, along with heuristics to quantify hallucination percentages. Experimental results on 4 travel-domain industry datasets demonstrate the effectiveness of GPT-4 in judging factual consistency between sources and generations. Additionally, tuning NLI models on synthetic data improves performance, while insights are provided on deployment latency and cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make sure language generation is trustworthy. When we create text using artificial intelligence, it’s important that the information is accurate. Researchers developed a way to test how faithful generated text is by comparing it to the original source. They used big models like GPT-4 to score how well the generated text matched the original. They also created fake data that included unfaithful information and found ways to measure how much of this type of data was present. The study shows that a certain model, GPT-4, is good at judging whether the generated text is accurate or not. It also suggests that training other models on fake data can make them better at doing this task. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Inference » Synthetic data
