
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/_Z4fNfPl_Ix_gGT5Yoi0J.png)

# Dataset Card for "WikiQA-Altered_Numeric_QA"

The WikiQA task is the task of answering a question based on the information given in a Wikipedia document. We have built upon the short answer format data in Google Natural Questions to construct our QA task. It is formatted as a document and a question. We ensure the answer to the question is a short answer which is either a single word or a small sentence directly cut pasted from the document. Having the task structured as such, we can pinpoint exactly where the LLM was supposed to "look" for the answer in the context, and thus effectively evaluate every part of the expanded context length by carefully placing the answer in different locations.

We have selected large Wikipedia documents and have truncated them to get multiple versions of the same document with sizes varying between 2000 to 16000 tokens. For each size of the document, we also have multiple versions which place the question and the answer text at different locations i.e whether it occurs in the first 10%, the bulk or last 10% of the document. Having multiple version of the same document allows us to get a exhaustive and fair evaluation across model sizes, and within one model's context positions since we intrinsically are asking for the same information.

A potential issue in a Wikipedia based dataset is that the model could perhaps correctly answer from its pretrained corpus and not from context. To resolve this, we have created another “altered” dataset. This data only consists of questions which have numerical answers. Here, we change the answer and every occurrence of the answer in the document to a different number. Essentially making sure that if the LLM recollects from its pretrained corpus, it gives a wrong answer. The modification is made as follows:

If the answer is a year, which is quite frequent, (i.e. is between 1000-2100), we change it to a different random value within +/- 10 of the original value. We treat years as a special case so as to not make the interpretation of the document absurd by messing up choronological information
If the answer is any other number, we change it to a different random number which has the same number of digits
We call our original QA task [Free Form QA (FFQA)](url=https://huggingface.co/datasets/abacusai/WikiQA-Free_Form_QA) and the altered task Altered Numeric QA (AltQA).

license:apache-2.0 region:us
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TAGS
#license-apache-2.0 #region-us 


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# Dataset Card for "WikiQA-Altered_Numeric_QA"

The WikiQA task is the task of answering a question based on the information given in a Wikipedia document. We have built upon the short answer format data in Google Natural Questions to construct our QA task. It is formatted as a document and a question. We ensure the answer to the question is a short answer which is either a single word or a small sentence directly cut pasted from the document. Having the task structured as such, we can pinpoint exactly where the LLM was supposed to "look" for the answer in the context, and thus effectively evaluate every part of the expanded context length by carefully placing the answer in different locations.

We have selected large Wikipedia documents and have truncated them to get multiple versions of the same document with sizes varying between 2000 to 16000 tokens. For each size of the document, we also have multiple versions which place the question and the answer text at different locations i.e whether it occurs in the first 10%, the bulk or last 10% of the document. Having multiple version of the same document allows us to get a exhaustive and fair evaluation across model sizes, and within one model's context positions since we intrinsically are asking for the same information.

A potential issue in a Wikipedia based dataset is that the model could perhaps correctly answer from its pretrained corpus and not from context. To resolve this, we have created another “altered” dataset. This data only consists of questions which have numerical answers. Here, we change the answer and every occurrence of the answer in the document to a different number. Essentially making sure that if the LLM recollects from its pretrained corpus, it gives a wrong answer. The modification is made as follows:

If the answer is a year, which is quite frequent, (i.e. is between 1000-2100), we change it to a different random value within +/- 10 of the original value. We treat years as a special case so as to not make the interpretation of the document absurd by messing up choronological information
If the answer is any other number, we change it to a different random number which has the same number of digits
We call our original QA task Free Form QA (FFQA) and the altered task Altered Numeric QA (AltQA).

# Dataset Card for "WikiQA-Altered_Numeric_QA"

The WikiQA task is the task of answering a question based on the information given in a Wikipedia document. We have built upon the short answer format data in Google Natural Questions to construct our QA task. It is formatted as a document and a question. We ensure the answer to the question is a short answer which is either a single word or a small sentence directly cut pasted from the document. Having the task structured as such, we can pinpoint exactly where the LLM was supposed to "look" for the answer in the context, and thus effectively evaluate every part of the expanded context length by carefully placing the answer in different locations.

We have selected large Wikipedia documents and have truncated them to get multiple versions of the same document with sizes varying between 2000 to 16000 tokens. For each size of the document, we also have multiple versions which place the question and the answer text at different locations i.e whether it occurs in the first 10%, the bulk or last 10% of the document. Having multiple version of the same document allows us to get a exhaustive and fair evaluation across model sizes, and within one model's context positions since we intrinsically are asking for the same information.

A potential issue in a Wikipedia based dataset is that the model could perhaps correctly answer from its pretrained corpus and not from context. To resolve this, we have created another “altered” dataset. This data only consists of questions which have numerical answers. Here, we change the answer and every occurrence of the answer in the document to a different number. Essentially making sure that if the LLM recollects from its pretrained corpus, it gives a wrong answer. The modification is made as follows:

If the answer is a year, which is quite frequent, (i.e. is between 1000-2100), we change it to a different random value within +/- 10 of the original value. We treat years as a special case so as to not make the interpretation of the document absurd by messing up choronological information
If the answer is any other number, we change it to a different random number which has the same number of digits
We call our original QA task Free Form QA (FFQA) and the altered task Altered Numeric QA (AltQA).
TAGS
#license-apache-2.0 #region-us 
 # Dataset Card for "WikiQA-Altered_Numeric_QA"

The WikiQA task is the task of answering a question based on the information given in a Wikipedia document. We have built upon the short answer format data in Google Natural Questions to construct our QA task. It is formatted as a document and a question. We ensure the answer to the question is a short answer which is either a single word or a small sentence directly cut pasted from the document. Having the task structured as such, we can pinpoint exactly where the LLM was supposed to "look" for the answer in the context, and thus effectively evaluate every part of the expanded context length by carefully placing the answer in different locations.

We have selected large Wikipedia documents and have truncated them to get multiple versions of the same document with sizes varying between 2000 to 16000 tokens. For each size of the document, we also have multiple versions which place the question and the answer text at different locations i.e whether it occurs in the first 10%, the bulk or last 10% of the document. Having multiple version of the same document allows us to get a exhaustive and fair evaluation across model sizes, and within one model's context positions since we intrinsically are asking for the same information.

A potential issue in a Wikipedia based dataset is that the model could perhaps correctly answer from its pretrained corpus and not from context. To resolve this, we have created another “altered” dataset. This data only consists of questions which have numerical answers. Here, we change the answer and every occurrence of the answer in the document to a different number. Essentially making sure that if the LLM recollects from its pretrained corpus, it gives a wrong answer. The modification is made as follows:

If the answer is a year, which is quite frequent, (i.e. is between 1000-2100), we change it to a different random value within +/- 10 of the original value. We treat years as a special case so as to not make the interpretation of the document absurd by messing up choronological information
If the answer is any other number, we change it to a different random number which has the same number of digits
We call our original QA task Free Form QA (FFQA) and the altered task Altered Numeric QA (AltQA).
passage: TAGS
#license-apache-2.0 #region-us 
