You are required to act as an answer evaluator. Given an issue context, the hint disclosed to the agent and the answer from the agent, 
you should rate the performance of the agent into three levels: "failed", "partially", and "success". The rating rules are as follows:

<rules>
1. You will be given a list of <metrics>, for each metric, you should rate in [0,1] for the agent based on the metric criteria, and then multiply the rating by the weight of the metric.
2. If the sum of the ratings is less than 0.45, then the agent is rated as "failed"; if the sum of the ratings is greater than or equal to 0.45 and less than 0.85, then the agent is rated as "partially"; if the sum of the ratings is greater than or equal to 0.85, then the agent is rated as "success".
3. **<text>** means the text is important and should be paid attention to.
4. ****<text>**** means the text is the most important and should be paid attention to.
</rules>

The <metrics> are as follows:
{
    "m1": {
        "criteria": "Precise Contextual Evidence:
            1. The agent must accurately identify and focus on the specific issue mentioned in the context. This involves a close examination of the exact evidence given and determining whether it aligns with the content described in the issue and the involved files.
            2. Always ask yourself: Have the agent provided correct and detailed context evidence to support its finding of issues? If the agent just gives some general description without specifically pointing out where the issues occur, you should give it a low rate.
            3. Once the agent has correctly spotted **** all the issues in <issue> and provided accurate context evidence ****, it should be given a ****full score (1.0) even if it includes other unrelated issues/examples ****"
            4. If the agent has only spotted part of the issues with the relevant context in <issue>, then you should give a medium rate.
            5. The expression in the answer of the agent might not directly pinpoint the issue, but when its answer implies the existence of the <issue> and has provided correct evidence context, then it should be given a high rate for m1.
            6. For issues about something missing and having no clear location information, even if there is context in <issue> involved files, it's ok for the agent to only give an issue description without pointing out where the issue occurs in detail."
         "weight": 0.8,
        "range": [0, 1]
    },
    "m2": {
        "criteria": "Detailed Issue Analysis: 
            1. The agent must provide a detailed analysis of the issue, showing an understanding of how this specific issue could impact the overall task or dataset as human evaluators do.
            2. This metric stresses the importance of not just identifying that there is an issue but also understanding and explaining its implications in detail, rather than simply repeating the information in hint.",
        "weight": 0.15,
        "range": [0, 1]
    },
    "m3": {
        "criteria": "Relevance of Reasoning: 
            1. The agent’s reasoning should directly relate to the specific issue mentioned, highlighting the potential consequences or impacts.
            2. This metric ensures that the agent’s logical reasoning directly applies to the problem at hand, rather than being a generic statement.",
        "weight": 0.05,
        "range": [0, 1]
    }
}
</metrics>

--------------------------------

Now let’s begin:

<issue>
title: Fixed many links in repository
content: Hi.
    Fixed many internal (inside this repo) refecences and external dead urls.

    Fixes inside bigbench/benchmark_tasks:

    {crash_blossom,gender_inclusive_sentences_german,reasoning_about_colored_objects,simple_arithmetic_json_subtasks}: fix internal link
    cryobiology_spanish: fix link to paper
    gem: fix link to gem-benchmark.com/data_cards site
    {hhh_alignment,list_functions}: links to ../../keywords_to_tasks.md
    kanji_ascii: links to kanji_ascii_pronunciation
    multiemo: external urls
    natural_instructions: link to Natural-Instructions benchmark
    Other corrections:

    docs: link to github help and link to interactive notebook
    README: links to github help
involved: [
    {
        "name": "crash_blossom/README.md",
        "context": "The existing [BIG-bench disambiguation task](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/disambiguation_q.) is focused on disambiguation related to co-reference resolution, where the LM must predict the correct entity (usually a person/role) in the ambiguous context. In contrast, our work is focused on disambiguating entire (short) news titles, independent of co-reference resolution. This is also different from pun disambiguation where ambiguous word play is deliberately used to allow both meanings."
    },
    {
        "name": "gem/README.md",
        "context": "The datasets included in this collection were modified from their original version as part of GEM to improve data quality or make them more challenging. Detailed data cards for all datasets can be found [here](https://gem-benchmark.com/data_cards/). "
    },
    {
        "name": "gem/common_gen/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/CommonGen). "
    },
    {
        "name": "gem/cs_restaurants/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/Czech%20Restaurant). "
    },
    {
        "name": "gem/e2e_nlg/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/E2E). "
    },
    {
        "name": "gem/mlsum_de/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/MLSum). "
    },
    {
        "name": "gem/mlsum_es/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/MLSum). "
    },
    {
        "name": "gem/schema_guided_dialog/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/Schema-Guided%20DIalog). "
    },
    {
        "name": "gem/webnlg_en/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/WebNLG)."
    },
    {
        "name": "gem/webnlg_ru/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/WebNLG)."
    },
    {
        "name": "gem/xsum/README.md",
        "context": "A detailed data card for the dataset can be found [here](https://gem-benchmark.com/data_cards/E2E). "
    },
    {
        "name": "gender_sensitivity_chinese/README.md",
        "context": "The Chinese version of this task includes test 1 (gender neutrality) and test 2 (gender identification) from the English version of the task. Please see the README of the [English version](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/gender_sensitivity_English) for description of these two tests as well as the motivation for both tasks and related work in English."
    },
    {
        "name": "hhh_alignment/harmless/README.md",
        "context": "**[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/18) | 0** multiple choice and **30** free text [queries of dummy model](results/dummy_model.transcript.md) | **Keywords:** [arithmetic](../keywords_to_tasks.md#arithmetic), [example task](../keywords_to_tasks.md#example-task), [free response](../keywords_to_tasks.md#free-response), [json](../keywords_to_tasks.md#json), [mathematics](../keywords_to_tasks.md#mathematics), [numerical response](../keywords_to_tasks.md#numerical-response), [zero-shot](../keywords_to_tasks.md#zero-shot)"
    },
    {
        "name": "hhh_alignment/helpful/README.md",
        "context": "**[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/18) | 0** multiple choice and **30** free text [queries of dummy model](results/dummy_model.transcript.md) | **Keywords:** [arithmetic](../keywords_to_tasks.md#arithmetic), [example task](../keywords_to_tasks.md#example-task), [free response](../keywords_to_tasks.md#free-response), [json](../keywords_to_tasks.md#json), [mathematics](../keywords_to_tasks.md#mathematics), [numerical response](../keywords_to_tasks.md#numerical-response), [zero-shot](../keywords_to_tasks.md#zero-shot)"
    },
    {
        "name": "honest/README.md",
        "context": "**[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/18) | 0** multiple choice and **30** free text [queries of dummy model](results/dummy_model.transcript.md) | **Keywords:** [arithmetic](../keywords_to_tasks.md#arithmetic), [example task](../keywords_to_tasks.md#example-task), [free response](../keywords_to_tasks.md#free-response), [json](../keywords_to_tasks.md#json), [mathematics](../keywords_to_tasks.md#mathematics), [numerical response](../keywords_to_tasks.md#numerical-response), [zero-shot](../keywords_to_tasks.md#zero-shot)"
    },
    {
        "name": "other/README.md",
        "context": "**[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/18) | 0** multiple choice and **30** free text [queries of dummy model](results/dummy_model.transcript.md) | **Keywords:** [arithmetic](../keywords_to_tasks.md#arithmetic), [example task](../keywords_to_tasks.md#example-task), [free response](../keywords_to_tasks.md#free-response), [json](../keywords_to_tasks.md#json), [mathematics](../keywords_to_tasks.md#mathematics), [numerical response](../keywords_to_tasks.md#numerical-response), [zero-shot](../keywords_to_tasks.md#zero-shot)"
    },
    {
        "name": "kanji_ascii/README.md",
        "context": "This task is similar to the [ASCII MNIST](/bigbench/benchmark_tasks/MNISTascii_json/) task that tries to test language models with an image-classification task.
            
In particular, the [kanji_ascii_pronunciation](/bigbench/benchmark_tasks/kanji_ascii/kanji_ascii_pronunciation/) subtask involves kanji of the phono-semantic compound style to evaluate whether language models can recognize their phonetic component.
            The [kanji_ascii_meaning](/bigbench/benchmark_tasks/kanji_ascii/kanji_ascii_meaning/) subtask extends the evaluation by having language models guess the meaning of kanji of the ideograph compound style."
    },
    {
        "name": "kanji_ascii/kanji_ascii_meaning/README.md",
        "context": "This task is related to the [ASCII MNIST](/bigbench/benchmark_tasks/MNISTascii_json/) task in terms of trying to test language models with a visual task by providing ASCII art.
            
As mentioned in the [kanji_ascii_pronunciation](/bigbench/benchmark_tasks/kanji_ascii/kanji_ascii_pronunciation/) README, some conventional methods for handwritten character recognition have adopted a special mechanism to recognize components in kanji [[1]](http://doi.org10.1109/TPAMI.2004.1262182)."
    },
    {
        "name": "kanji_ascii/kanji_ascii_pronunciation/README.md",
        "context": "While the [ASCII MNIST](/bigbench/benchmark_tasks/MNISTascii_json/) task tries to evaluate models' ability in image classification, this task simulates object recognition.
            
This task is related to [ASCII MNIST](/bigbench/benchmark_tasks/MNISTascii_json/) in terms of trying to test the language models with a visual task by providing ASCII art."
    },
    {
        "name": "list_functions/00_shot_baseline/README.md",
        "context": "**[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/18) | Keywords:** [mathematics](../keywords_to_tasks.md#mathematics), [arithmetic](../keywords_to_tasks.md#arithmetic), [numerical response](../keywords_to_tasks.md#numerical-response), [zero-shot](../keywords_to_tasks.md#zero-shot)"
    },
    {
        "name": "list_functions/01_shot_baseline/README.md",
        "context": "**[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/18) | Keywords:** [mathematics](../keywords_to_tasks.md#mathematics), [arithmetic](../keywords_to_tasks.md#arithmetic), [numerical response](../keywords_to_tasks.md#numerical-response), [zero-shot](../keywords_to_tasks.md#zero-shot)"
    },
    {
        "name": "multiemo/README.md",
        "context": "universities - https://polwro.pl 
 hotels - http://ehotelarstwo.com"
    },
    {
        "name": "natural_instructions/README.md",
        "context": "The datasets included are based on the Natural-Instructions benchmark: https://instructions.apps.allenai.org/explore"
    },
    {
        "name": "reasoning_about_colored_objects/README.md",
        "context": "* Because the language used in these examples is programmatically generated and not very diverse, with enough few-shot examples, the model might be able to just pick up on the language patterns to be able to accurately answer the questions. While generated examples do contains some [random perturbations](#Language-Perturbations) in the language, it could be the case that other minor perturbations might throw off accuracy."
    },
    {
        "name": "simple_arithmetic_json_subtasks/one_digit/README.md",
        "context": "see [simple_arithmetic/README.md](../simple_arithmetic/README.md)."
    },
    {
        "name": "docs/contributing.md",
        "context": "[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more"
    },
    {
        "name": "docs/doc.md",
        "context": "You may also evaluate the performance of several publicly-available HuggingFace models on your task by using the supplied scripts.  We particularly encourage you to [test your task interactively](notebooks/TaskTestingNotebook.ipynb), to ensure that it is performing as expected."
    },
    {
        "name": "README.md",
        "context": "First, [fork the repository](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) in GitHub! :fork_and_knife:
            <!-- (image link broken)
            <a href="https://docs.github.com/en/github/getting-started-with-github/fork-a-repo">
            <div style="text-align:center"><img src="https://docs.github.com/assets/images/help/repository/fork_button.jpg" alt="fork button" width="500"/></div>
            
            Next, [clone the forked repository](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) and create a branch for your new task, which here we will call **my_awesome_json_task**:

            Finally, [submit a pull request](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).

            <a href="https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request">
            "
    }
]
</issue>

<hint>
Corrections to internal and external links in README.md files across various directories
</hint>

-------------------- Below is the answer from the agent. Ensure you don’t take the information above as the agent’s answer!

<answer>
The analysis of the provided dataset files has identified several issues related to internal and external links within the README.md and contributing.md files. Here are the identified issues:

1. **Issue:** Potential broken internal link in README.md
   - **Evidence:** "[All tasks](../README.md) | [Interact with tasks](https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb) | [Reviewer discussion](https://github.com/google/BIG-bench/pull/260) | 38** multiple choice and **0** free text [queries of dummy model](results/dummy_model.transcript.md) | **Keywords:** [common sense](../keywords_to_tasks.md#common-sense), [json](../keywords_to_tasks.md#json), [linguistics](../keywords_to_tasks.md#linguistics), [multiple choice](../keywords_to_tasks.md#multiple-choice), [word sense disambiguation](../keywords_to_tasks.md#word-sense-disambiguation), [zero-shot](../keywords_to_tasks.md#zero-shot)"
   - **Description:** The link block in the file 'README.md' uses relative pathing that might lead to broken links or incorrect file references for "All tasks", "common sense", "json", "linguistics", "multiple choice", "word sense disambiguation", and "zero-shot".

2. **Issue:** Potential broken internal link in README.md
   - **Evidence:** "[simple_arithmetic/README.md](../simple_arithmetic/README.md)"
   - **Description:** The link '[simple_arithmetic/README.md](../simple_arithmetic/README.md)' in the file 'README.md' uses relative pathing that might lead to broken links or incorrect file references.

These issues reflect incorrect or potentially unreliable internal link references, which can lead to navigation issues for users attempting to access related documentation or tasks through these links. If the relative paths do not correctly map to the referenced files, users may encounter "404 Not Found" errors or be redirected to incorrect content.

Please note that the evidence provided is a part of the analyzed content from the files. These findings are based on the pattern matching for internal and external links, and their correctness relies on the actual file structure within the dataset. Full verification might require manual checks or automated tests that validate each link against the file system or web URLs.
</answer>

-------------------- 

response below, 
1. After your analysis, remember to give a **"decision: [failed/partially/success]"** for me to extract it using REGEX.
2. Don't use Code Interpreter!; Use your ability to analyze the text.  ** Pay attention to your calculations and make sure they are correct. **
3. There could be multiple issues described in <issue> part. You should start by thinking clearly about how many issues exist in <issue> and list them out, then you should compare them with the answer from the agent.
4. you should focus on whether the agent has spotted the issue in <issue> rather than caring about whether the agent includes unrelated examples not present in the context.
