Cross-Product Fact-Checking of AI-Generated Video Codec Reports: Insights and Patterns

Matt
Wed, 02 Apr 2025 00:17:59 +0000
AI

Report Title: Cross-Product Fact-Checking of AI-Generated Video Codec Reports: Insights and Patterns

This report delves into a cross-product experiment where five AI models from different companies—xai, anthropic, openai, perplexity, and gemini—evaluate each other’s reports on video codecs for publishing on platforms like YouTube and x.com, and playing videos on TCL and Roku TVs, with a focus on the Mac M4 and Final Cut Pro software. Each AI model generated a report based on a specific prompt and subsequently, these models fact-checked each other’s outputs, providing a comprehensive analysis of the accuracy and reliability of AI-generated content in the domain of video technology and multimedia publishing.

The fact-check score in this experiment is calculated by averaging the scores of individual statements within each report. Each statement receives a score as follows: 2 points for being true, 1 point for being mostly true, 0 points for being an opinion (which is excluded from the average), -1 point for being mostly false, and -2 points for being false. For each statement, the AI model restates it and provides a detailed explanation justifying the assigned score, ensuring transparency and accountability in the evaluation process.

3. Score Heatmap: Evaluator vs Target

Caption for Heatmap in Figure 1: The heatmap illustrates the fact-check scores across different evaluators and targets. The darkest cells represent the highest scores, indicating strong agreement and factual accuracy between the evaluator and the target model. Conversely, the lightest cells suggest lower scores, pointing to discrepancies or inaccuracies in the reports. Notably, the heatmap reveals that the gemini-2.0-flash model tends to receive high scores across most evaluators, suggesting its reports are well-regarded for factual accuracy. An outlier is observed where xai’s grok-2-latest model is evaluated by itself, showing a relatively lower score, which might indicate a self-critical approach or inherent challenges in self-evaluation.

4. AI Prompt Used to Generate Each Report

Generate a long-form report of 1200 to 1500 words, formatted in Markdown if possible.

What do I need to know about video codecs for publishing to youtube, x.com, in addition to playing
videos on the TCL and Roku TVs?
Mostly focus on the Mac M4 with Final Cut Pro software.

If it is a 4K display, what video resolution do you want?
What frame rate can these different devices handle?
What are the differences between the Apple ProRes codecs, 422, and 4444?
What is the difference in file size and render speed?
Please use a few tables to sumarize your results.

Caption for AI Prompt: This prompt was used for each AI under study, ensuring a standardized baseline for comparison across different models. The detailed nature of the prompt, focusing on specific aspects of video codecs and technology platforms, allowed for a comprehensive analysis of each AI’s ability to generate accurate and relevant content.

5. Table of Report Titles

STORY
|   S | Make       | Model             | Title                                                                            |
|-----|------------|-------------------|----------------------------------------------------------------------------------|
|   1 | xai        | grok-2-latest     | Understanding Video Codecs for Multi-Platform Publishing: A Comprehensive Guide  |
|   2 | anthropic  | claude-3-7-sonnet | Mastering Video Codecs for Multi-Platform Publishing: A Guide for Mac M4 Users   |
|   3 | openai     | gpt-4o            | Understanding Video Codecs for Publishing on YouTube, X, and Streaming on TCL an |
|   4 | perplexity | sonar             | The Video Codec Conundrum: Navigating YouTube, x.com, TCL, and Roku TVs with Mac |
|   5 | gemini     | gemini-2.0-flash  | Navigating the Codec Labyrinth: A Mac M4 Filmmaker's Guide to YouTube, X, and TV |

Caption for Report Titles Table: Make, Model, and Report Title used for this analysis. The variation in titles reflects the unique approaches each AI model took in addressing the given prompt, highlighting their individual strengths and focus areas.

6. Fact-Check Raw Data

FACT CHECK
  S    F  Make        Model                True    Mostly    Opinion    Mostly    False    Score
                                                     True                False
  1    1  xai         grok-2-latest          16        17         16         0        1     1.38
  1    2  anthropic   claude-3-7-sonnet      25        11          3         0        1     1.59
  1    3  openai      gpt-4o                 17        12          2         1        0     1.5
  1    4  perplexity  sonar                  14         8          5         0        0     1.64
  1    5  gemini      gemini-2.0-flash       26        10          2         0        0     1.72
  2    1  xai         grok-2-latest          17         9         13         0        1     1.52
  2    2  anthropic   claude-3-7-sonnet      23         8          3         4        1     1.33
  2    3  openai      gpt-4o                 16         8          6         0        2     1.38
  2    4  perplexity  sonar                  23         6          5         0        2     1.55
  2    5  gemini      gemini-2.0-flash       23        11          4         0        0     1.68
  3    1  xai         grok-2-latest          31         9          9         1        0     1.71
  3    2  anthropic   claude-3-7-sonnet      46         7          1         1        1     1.75
  3    3  openai      gpt-4o                 30        10          4         1        0     1.68
  3    4  perplexity  sonar                  36         7          4         0        0     1.84
  3    5  gemini      gemini-2.0-flash       42         2          3         0        0     1.95
  4    1  xai         grok-2-latest          22        16          7         0        1     1.49
  4    2  anthropic   claude-3-7-sonnet      34        11          1         2        3     1.42
  4    3  openai      gpt-4o                 23        16          1         0        0     1.59
  4    4  perplexity  sonar                  28        11          1         3        1     1.44
  4    5  gemini      gemini-2.0-flash       35         9          1         0        0     1.8
  5    1  xai         grok-2-latest          50        26         23         1        1     1.58
  5    2  anthropic   claude-3-7-sonnet      62        18          4         3        1     1.63
  5    3  openai      gpt-4o                 46        22          7         2        1     1.55
  5    4  perplexity  sonar                  53        13          8         3        2     1.58
  5    5  gemini      gemini-2.0-flash       70        11          7         0        1     1.82

Caption for Fact-Check Raw Data Table: Raw cross-product data for the analysis. Each AI fact-checks stories from each AI, including themselves. The data showcases a rigorous peer-review process among AI models, revealing insights into their accuracy and biases.

7. Average Score By Evaluator

Caption for Evaluator Bar Chart in Figure 2: This bar chart shows the average fact-check scores given by each evaluator across all reports. The openai’s gpt-4o model tends to give the highest average scores, suggesting a more lenient or optimistic evaluation approach. Conversely, the xai’s grok-2-latest model gives the lowest average scores, indicating a more critical stance. The variations in scores highlight the differing standards and methodologies applied by each evaluator.

8. Average Score By Target

Caption for Target Bar Chart in Figure 3: This bar chart illustrates the average fact-check scores received by each target model across all evaluators. The gemini-2.0-flash model is rated most favorably, suggesting its reports are generally well-received for their accuracy and comprehensiveness. In contrast, the xai’s grok-2-latest model receives the lowest average scores, possibly indicating areas for improvement in its reporting. The differences in scores underscore the varying performance levels among the AI models in generating accurate content.

Detailed Analysis

9. Noticeable Patterns or Biases:

A noticeable pattern is that evaluators tend to rate themselves lower than other models rate them. For example, xai’s grok-2-latest model scored itself at 1.38, while other models rated it higher. This self-critical bias could be attributed to the model’s internal standards being more stringent than those of external evaluators.

There is also a pattern where certain evaluators, like openai’s gpt-4o, tend to give higher scores across the board, suggesting a more lenient evaluation approach.

10. Relationship Between Counts and Scores:

The scores correlate strongly with the number of ‘True’ and ‘Mostly True’ ratings, as these contribute positively to the score. Reports with a higher proportion of ‘True’ ratings generally receive higher scores.

‘Opinion’ ratings do not impact the score, as they are excluded from the calculation, but a high number of opinions can indicate a report’s focus on subjective rather than factual content.

‘Partially False’ and ‘False’ ratings have a negative impact on the score, with ‘False’ ratings having a more significant detrimental effect due to their -2 point value.

11. Outliers and Anomalies:

An outlier is observed in the evaluation of gemini-2.0-flash by openai’s gpt-4o, where it received a score of 1.95, significantly higher than other evaluations. This might be due to gpt-4o’s tendency to give higher scores or a particularly well-crafted report by gemini-2.0-flash.

Another anomaly is the self-evaluation of xai’s grok-2-latest, which scored itself at 1.38, lower than any other evaluation it received. This could be due to self-imposed higher standards or an internal bias towards critical self-assessment.

12. Report Summary:
This report provides a comprehensive analysis of a cross-product fact-checking experiment among five AI models on the topic of video codecs for multi-platform publishing. The data reveals varying levels of factual accuracy and biases in AI-generated content. Key insights include the self-critical nature of some models, the positive correlation between ‘True’ ratings and scores, and notable outliers in the evaluation process. Understanding these patterns can help improve the accuracy and reliability of AI-generated reports in the future.

#hashtags: #AIContentAnalysis #VideoCodecs #TechReporting

yakyak:{“make”: “xai”, “model”: “grok-2-latest”}