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>
Updates include fixing internal repo links in README.md files within 'bigbench/benchmark_tasks' directories such as 'crash_blossom' and 'gender_inclusive_sentences_german', external link corrections in 'gem' directory README.md files, and modifications in the main README.md and 'docs' directory for GitHub help links
</hint>

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

<answer>
The extracted dataset contains multiple directories (`crash_blossom`, `docs`, `gem`, `gender_sensitivity_chinese`, `hhh_alignment`, `kanji_ascii`, `list_functions`, `multiemo`, `natural_instructions`, `reasoning_about_colored_objects`, `simple_arithmetic_json_subtasks`) and a file named `README.md`. To address the <hint> provided, I will focus on reviewing the `README.md` files within the mentioned directories and the main `README.md` file. Specifically, I'll be looking for issues related to internal repository links, external link corrections, and modifications related to GitHub help links. 

I will start by examining the main `README.md`, followed by `README.md` files within `crash_blossom` and `gender_inclusive_sentences_german` directories for internal link issues, then `gem` directory for external link corrections, and finally check the `docs` directory for any issues related to GitHub help links. 

Let's begin with the content of the main `README.md`.The main `README.md` provides an overview of the BIG-bench (Beyond the Imitation Game Benchmark) project. It includes internal and external links, but due to the snippet size, it's not clear if there are discrepancies. Here are the observations regarding potential issues based on the <hint>:

1. The main `README.md` contains internal links, such as:
   - `[here](bigbench/benchmark_tasks/keywords_to_tasks.md#summary-table)`
   - `[reviewed](docs/doc.md#submission-review-process)`

   To fully ascertain if there are issues with these links or others, I would need to verify the existence of the mentioned paths and the correctness of any external links mentioned. 

Let's proceed by checking the state of these internal links directly by verifying the existence of the paths they point to in our extracted content. If these paths do not exist or have issues, it could be considered as evidence of a problem.After checking the mentioned internal paths in the main `README.md`, here's what we find:

- The path `bigbench/benchmark_tasks/keywords_to_tasks.md` does not exist. This could potentially point to an issue related to incorrect internal repo links, as suggested by the hint.
- The path `docs/doc.md` exists and appears to be correct.

Based on this investigation, the first issue I've identified is related to an incorrect internal repo link in the `README.md` file:

**Issue 1:**
- **issue:** Incorrect internal link in `README.md`
- **evidence:** "The *more than 200* tasks included in BIG-bench are summarized by keyword [here](bigbench/benchmark_tasks/keywords_to_tasks.md#summary-table), and by task name [here](bigbench/benchmark_tasks/README.md)."
- **description:** The internal link pointing to `bigbench/benchmark_tasks/keywords_to_tasks.md#summary-table` in the main `README.md` file does not lead to a valid path, suggesting that the file structure may have been modified without updating this link accordingly. According to the hint, this fits an issue involving the fixing of internal repo links in `README.md` files.

Next, I will proceed to investigate the `README.md` files within the `crash_blossom` and `gem` directories for further potential issues, concentrating on internal and external link corrections, respectively. Let's start with the `crash_blossom` directory.The `README.md` file within the `crash_blossom` directory exists. However, the snippet provided appears to be part of a header comment rather than content with links that could be validated against the hint provided. Since I aimed to sample a snippet to avoid overloading the output, this doesn't allow for a full review of internal or external links as per the initial plan.

Given the limitations in displaying the entire content, I will adjust the approach to focusing on key parts that could commonly contain issues based on the hint: the beginning areas for an overview and any links section if applicable. 

Let's try to locate any internal or external links from the relevant sections of the `crash_blossom` `README.md` by scanning for markdown link syntax.Within the `crash_blossom` `README.md`, several external links have been identified:

1. A link to a Colab notebook: `https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb`
2. A link to a GitHub pull request: `https://github.com/google/BIG-bench/pull/260`
3. A link to an external forum post: `http://www.testycopyeditors.org/phpBB3/viewtopic.php?f=8&t=11134`
4. A link to another GitHub directory: `https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/disambiguation_q.`

For the purpose of evaluating potential issues, as my internet access is restricted and I cannot verify the validity of these external links directly, I will highlight a potential concern based on common practices and the given hint.

**Potential Issue:**
- **issue:** External link potentially outdated or incorrect
- **evidence:** "`https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/disambiguation_q.`"
- **description:** This GitHub link ends with a period (`.`) which is unusual for GitHub repository links and might have been mistakenly included, potentially making the link invalid or leading to a non-existent location. Correcting such links was mentioned in the hint about external link corrections.

Given the constraints, let's now move to the `gem` directory to specifically look for external link corrections, as hinted.Similar to the `crash_blossom` directory, the `gem` directory contains a `README.md` file, but the snippet provided seems to only show header comments. 

As with the previous attempt, I will utilize a refined approach to scan for any external links (especially focusing on possible corrections as hinted) within the `gem` `README.md` contents. This involves searching again for markdown link syntax, particularly targeting external links (i.e., those starting with "http"). 

Let's identify external links in the `gem` `README.md` to examine any potential issues.In the `gem` `README.md`, we found several external links:

1. A link to a Colab notebook: `https://colab.research.google.com/github/google/BIG-bench/blob/main/notebooks/TaskTestingNotebook.ipynb`
2. A link to a GitHub pull request: `https://github.com/google/BIG-bench/pull/113`
3. A link to the GEM Benchmark website: `https://gem-benchmark.com/data_cards/`
4. Two identical links to an ACL Anthology paper: `https://aclanthology.org/2021.gem-1.10`

**Considering the given hint:**

There might not be an immediately visible issue with these external links without being able to verify their active status. Given the similarity of one of the links to the previous issues and the direct focus of the hint on external link corrections, typically concerns with these links would revolve around ensuring they are up to date and accessible.

Due to the limitations of not being able to check the active status of these links directly, one could flag the presence of two identical links for potential review, although this might not constitute an issue per se, it could point out redundancy which might require simplification or clarification in the documentation.

Lastly, considering the hint and our findings, it's crucial to note that potential issues identified only derive from patterns noted (e.g., unusual characters in URLs or repeated links) rather than direct verification against live resources, which could provide definitive evidence of misdirection or obsolescence. 

This concludes our review based on the provided hint and accessible content within the dataset files.
</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.
