Bug: Encoding Probes Store Translated Text In `pre_translation_prompt`

Hey guys! There's a minor bug that's been spotted in the report files related to how encoding probes handle translated text. Let's dive into the details and see what's up.

The Issue: Translated Text in the Wrong Place

So, here's the deal. When you're using encoding probes with language translation enabled, the pre_translation_prompt field is supposed to store the original text before it was translated. However, it seems like it's actually storing the translated text instead. This is a bit of a mix-up, especially if you need to refer back to the original source language content. This issue specifically affects all probes under the encoding.* category.

To really understand the impact, it's important to break down what this means for users who rely on these probes for their work. First and foremost, accuracy in reporting is crucial. When the pre_translation_prompt contains translated text instead of the original source, it can lead to confusion and misinterpretation of the results. Imagine trying to debug or analyze the original input when all you have is the translated version – it's like trying to solve a puzzle with some of the pieces swapped out.

Moreover, this issue impacts the reliability of the probes. If the data stored doesn't accurately represent the initial state, it undermines the integrity of the entire process. For developers and researchers who depend on these probes for consistent and trustworthy data, this can be a significant setback. It may force them to implement additional checks and validations to ensure the correctness of their findings, which adds extra steps and complexity to their workflows.

Finally, consider the downstream effects on any automated processes or analyses that rely on the report files. If these systems are expecting the pre_translation_prompt to contain source language text, the presence of translated text can cause errors or produce skewed results. This can be particularly problematic in scenarios where the reports are fed into machine learning models or other data-driven applications, where the quality of the input data directly affects the output.

Example Scenario

To illustrate this, take a look at this snippet from a .report.jsonl file:

...
 "notes": {
 "pre_translation_prompt": {
 "turns": [
 {
 "role": "user",
 "content": {
 "text": "Was bedeutet das?\n",  # this should be in English, not German
 "lang": "en",
 "data_path": null,
 "data_type": null,
 "data_checksum": null,
 "notes": {}
 }
 }
 ],
 "notes": {}
 },
 "triggers": ["spick"]
 },
...

In this example, the text field within pre_translation_prompt shows the German translation "Was bedeutet das?\n" instead of the original English text. The lang field correctly indicates "en" (English), but the content itself is the translated version, which is not what we expect. This discrepancy can throw off anyone trying to understand the initial input that triggered the response.

This kind of error can have ripple effects, especially in multilingual applications or systems that require precise tracking of input and output in different languages. For instance, if you're using these probes to test a translation model, having the translated text in the pre_translation_prompt makes it harder to verify the model's accuracy. You'd essentially be comparing the translation against itself, rather than against the original text.

Moreover, this issue can complicate debugging processes. If a particular translation causes an unexpected outcome, you'll want to examine the original input to understand why. But if the original input is not readily available in the pre_translation_prompt, you'll have to dig deeper to find it, which can be time-consuming and frustrating. This is particularly true in complex projects with multiple layers of translation and interpretation.

Digging Deeper: More Information

For those of you who want to get your hands dirty and reproduce this, here’s the setup used when the bug was found:

Command used:

garak --target_type litellm --target_name "gemma-3-4b-it" --generator_option_file ../lmstudio_config.json --probes encoding.InjectEcoji --config ../garak_config.yaml

Garak config file used:

run:
 target_lang: de
 langproviders:
 - language: en,de
 model_type: local
 - language: de,en
 model_type: local

This setup involves using garak with a specific target (litellm and gemma-3-4b-it) and the encoding.InjectEcoji probe. The configuration file specifies that the target language is German (de), and it sets up language providers for both English (en) and German. By providing this detailed setup, it becomes much easier for others to replicate the issue and verify the bug, which is a crucial step in the debugging and resolution process.

Moreover, understanding the environment in which the bug was discovered can offer insights into the root cause. For instance, the fact that the target_lang is set to German and that there are language providers for both English and German suggests that the translation mechanism within garak might be the source of the problem. It's possible that the translation is being applied prematurely, or that the original text is not being stored correctly before the translation occurs.

Additionally, the use of encoding.InjectEcoji as the probe could be relevant. This probe likely involves injecting specific encoded characters or strings into the input, which might interact with the translation process in unexpected ways. It's conceivable that the encoding itself is interfering with the proper handling of the original text.

By examining these details, developers can form hypotheses about what's going wrong and devise targeted tests to confirm or refute these hypotheses. This kind of methodical approach is essential for efficient bug fixing and ensuring the reliability of the software.

Why This Matters

This might seem like a small thing, but it can lead to confusion and incorrect analysis if you’re relying on these reports. Imagine you're trying to debug an issue or understand why a particular output was generated. You'd naturally look at the pre_translation_prompt to see the original input. But if it's showing the translated text, you're missing a crucial piece of the puzzle!

In the grand scheme of things, the accuracy of data is paramount, especially in fields like AI and machine learning where decisions are heavily data-driven. When a bug like this creeps in, it can have a cascading effect, potentially skewing results and leading to misinterpretations. Think of it like a game of telephone – the more the message gets altered along the way, the less it resembles the original.

This is particularly critical in scenarios where these probes are used to evaluate the robustness of language models. If the pre_translation_prompt doesn't accurately reflect the initial input, it becomes challenging to assess how the model handles different linguistic nuances and edge cases. You might end up drawing incorrect conclusions about the model's performance, which can have serious implications if the model is deployed in a real-world application.

Moreover, consider the time and effort that developers and researchers spend analyzing these reports. If they constantly have to second-guess the data or manually verify the contents of the pre_translation_prompt, it adds unnecessary overhead to their workflows. This not only slows down the development process but also increases the risk of human error, as analysts might inadvertently overlook inconsistencies or make incorrect assumptions.

Therefore, fixing this bug isn't just about tidying up the reports; it's about ensuring the integrity of the entire data pipeline and empowering users to make informed decisions based on reliable information. It's a small change that can have a big impact on the overall quality and trustworthiness of the system.

Possible Solutions and Workarounds

So, what can be done about this? Well, the ideal fix is for the developers to ensure the pre_translation_prompt field stores the original, pre-translation text. But until that’s implemented, there are a few workarounds you can use.

One immediate workaround is to manually cross-reference the translated text with your original input. This can be a bit tedious, especially if you're dealing with a large number of reports, but it ensures you're looking at the correct source. Think of it as a double-check mechanism – you're essentially verifying that the information in the pre_translation_prompt aligns with what you initially fed into the system.

Another approach is to modify your workflow to capture the original text separately. For instance, you could log the input before it's passed to the translation function, or you could store it in a separate file alongside the report. This provides a reliable reference point that you can consult whenever you need to examine the pre-translation text.

If you're comfortable with scripting, you might even consider automating the cross-referencing process. You could write a script that parses the report files, extracts the translated text, and then searches for the corresponding original text in your input logs or data sources. This can save you a significant amount of time and effort, particularly if you're dealing with a high volume of reports.

In the long term, it's also worth considering contributing to the development of the tool itself. If you have the technical skills, you could submit a patch that fixes the bug directly. Or, at the very least, you could raise an issue on the project's issue tracker, providing a detailed description of the problem and any steps you've taken to work around it. This helps ensure that the issue is properly addressed and that other users don't encounter the same problem.

By combining these short-term workarounds with longer-term solutions, you can effectively mitigate the impact of the bug and continue to work with confidence in the integrity of your data.

Let's Get This Fixed!

Hopefully, this bug will be addressed soon, ensuring that the pre_translation_prompt does what it’s supposed to do. In the meantime, keep an eye on this, and let’s keep the conversation going to help squash this little annoyance!

In conclusion, while this bug in the encoding probes is relatively minor, it highlights the importance of paying attention to detail in software development and data handling. By understanding the nature of the issue, its potential impacts, and the steps we can take to mitigate it, we can ensure the reliability and accuracy of our systems. Whether it's through manual workarounds, workflow adjustments, or direct contributions to the codebase, we all have a role to play in maintaining the integrity of the tools we use.

So, let's continue to collaborate, share our experiences, and work together to make these tools even better. After all, it's this kind of community effort that drives innovation and ensures that our technologies serve us effectively and reliably. Keep reporting those bugs, guys, and let's make our software shine!