HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning
Paper • 2503.00912 • Published • 3
Datasets:
id stringclasses 179
values | question stringlengths 8.75k 85.9k | answer dict |
|---|---|---|
1909.00694 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the seed lexicon?
Context: <<<Title>>>
Minimally Supervised Learning of Affective Events Using Discourse Relations
<<<Abstract>>>
Recognizing affective events that trigger positive ... | {
"references": [
"seed lexicon consists of positive and negative predicates"
],
"type": "extractive"
} |
1909.00694 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are labels available in dataset for supervision?
Context: <<<Title>>>
Minimally Supervised Learning of Affective Events Using Discourse Relations
<<<Abstract>>>
Recognizing affective e... | {
"references": [
"negative,positive"
],
"type": "extractive"
} |
1909.00694 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How large is raw corpus used for training?
Context: <<<Title>>>
Minimally Supervised Learning of Affective Events Using Discourse Relations
<<<Abstract>>>
Recognizing affective events that ... | {
"references": [
"100 million sentences"
],
"type": "extractive"
} |
1910.14497 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is embedding quality assessed?
Context: <<<Title>>>
Probabilistic Bias Mitigation in Word Embeddings
<<<Abstract>>>
It has been shown that word embeddings derived from large corpora ten... | {
"references": [
"We compare this method of bias mitigation with the no bias mitigation (\"Orig\"), geometric bias mitigation (\"Geo\"), the two pieces of our method alone (\"Prob\" and \"KNN\") and the composite method (\"KNN+Prob\"). We note that the composite method performs reasonably well according the the ... |
1912.02481 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What turn out to be more important high volume or high quality data?
Context: <<<Title>>>
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\`ub\'a and Twi
<<<... | {
"references": [
"only high-quality data helps",
"high-quality"
],
"type": "extractive"
} |
1912.02481 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What two architectures are used?
Context: <<<Title>>>
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\`ub\'a and Twi
<<<Abstract>>>
The success of several a... | {
"references": [
"fastText,CWE-LP"
],
"type": "extractive"
} |
2002.02224 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is quality of the citation measured?
Context: <<<Title>>>
Citation Data of Czech Apex Courts
<<<Abstract>>>
In this paper, we introduce the citation data of the Czech apex courts (Supre... | {
"references": [
"it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition mo... |
2003.06651 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is the method described in this work a clustering-based method?
Context: <<<Title>>>
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
<... | {
"references": [
"Yes"
],
"type": "boolean"
} |
2003.06651 | "Please extract a concise answer without any additional explanation for the following question based(...TRUNCATED) | {"references":["The contexts are manually labelled with WordNet senses of the target words"],"type":(...TRUNCATED) |
2003.06651 | "Please answer the following question with yes or no based on the given text. You only need to outpu(...TRUNCATED) | {
"references": [
"Yes"
],
"type": "boolean"
} |
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This repository contains the data presented in HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning.
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