PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering Article Swipe
Yiming Du
,
Hongru Wang
,
Zhengyi Zhao
,
Bin Liang
,
Baojun Wang
,
Wanjun Zhong
,
Zezhong Wang
,
Kam‐Fai Wong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.16288
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.16288
Long-term memory plays a critical role in personal interaction, considering long-term memory can better leverage world knowledge, historical information, and preferences in dialogues. Our research introduces PerLTQA, an innovative QA dataset that combines semantic and episodic memories, including world knowledge, profiles, social relationships, events, and dialogues. This dataset is collected to investigate the use of personalized memories, focusing on social interactions and events in the QA task. PerLTQA features two types of memory and a comprehensive benchmark of 8,593 questions for 30 characters, facilitating the exploration and application of personalized memories in Large Language Models (LLMs). Based on PerLTQA, we propose a novel framework for memory integration and generation, consisting of three main components: Memory Classification, Memory Retrieval, and Memory Synthesis. We evaluate this framework using five LLMs and three retrievers. Experimental results demonstrate that BERT-based classification models significantly outperform LLMs such as ChatGLM3 and ChatGPT in the memory classification task. Furthermore, our study highlights the importance of effective memory integration in the QA task.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.16288
- https://arxiv.org/pdf/2402.16288
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392222355
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392222355Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2402.16288Digital Object Identifier
- Title
-
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question AnsweringWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-26Full publication date if available
- Authors
-
Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong Wang, Kam‐Fai WongList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.16288Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.16288Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2402.16288Direct OA link when available
- Concepts
-
Term (time), Question answering, Long-term memory, Computer science, Information retrieval, Artificial intelligence, Natural language processing, Psychology, Cognition, Neuroscience, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4392222355 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2402.16288 |
| ids.doi | https://doi.org/10.48550/arxiv.2402.16288 |
| ids.openalex | https://openalex.org/W4392222355 |
| fwci | |
| type | preprint |
| title | PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9973000288009644 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Topic Modeling |
| topics[1].id | https://openalex.org/T10181 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9753000140190125 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Natural Language Processing Techniques |
| topics[2].id | https://openalex.org/T12031 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9097999930381775 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Speech and dialogue systems |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C61797465 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7393597364425659 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1188986 |
| concepts[0].display_name | Term (time) |
| concepts[1].id | https://openalex.org/C44291984 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6310719847679138 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1074173 |
| concepts[1].display_name | Question answering |
| concepts[2].id | https://openalex.org/C24590219 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6154009699821472 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q18601 |
| concepts[2].display_name | Long-term memory |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.583726704120636 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C23123220 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5672065615653992 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q816826 |
| concepts[4].display_name | Information retrieval |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3399444818496704 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C204321447 |
| concepts[6].level | 1 |
| concepts[6].score | 0.333395779132843 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[6].display_name | Natural language processing |
| concepts[7].id | https://openalex.org/C15744967 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2664610743522644 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[7].display_name | Psychology |
| concepts[8].id | https://openalex.org/C169900460 |
| concepts[8].level | 2 |
| concepts[8].score | 0.1207427978515625 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2200417 |
| concepts[8].display_name | Cognition |
| concepts[9].id | https://openalex.org/C169760540 |
| concepts[9].level | 1 |
| concepts[9].score | 0.10142171382904053 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[9].display_name | Neuroscience |
| concepts[10].id | https://openalex.org/C121332964 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[10].display_name | Physics |
| concepts[11].id | https://openalex.org/C62520636 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[11].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/term |
| keywords[0].score | 0.7393597364425659 |
| keywords[0].display_name | Term (time) |
| keywords[1].id | https://openalex.org/keywords/question-answering |
| keywords[1].score | 0.6310719847679138 |
| keywords[1].display_name | Question answering |
| keywords[2].id | https://openalex.org/keywords/long-term-memory |
| keywords[2].score | 0.6154009699821472 |
| keywords[2].display_name | Long-term memory |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.583726704120636 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/information-retrieval |
| keywords[4].score | 0.5672065615653992 |
| keywords[4].display_name | Information retrieval |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.3399444818496704 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/natural-language-processing |
| keywords[6].score | 0.333395779132843 |
| keywords[6].display_name | Natural language processing |
| keywords[7].id | https://openalex.org/keywords/psychology |
| keywords[7].score | 0.2664610743522644 |
| keywords[7].display_name | Psychology |
| keywords[8].id | https://openalex.org/keywords/cognition |
| keywords[8].score | 0.1207427978515625 |
| keywords[8].display_name | Cognition |
| keywords[9].id | https://openalex.org/keywords/neuroscience |
| keywords[9].score | 0.10142171382904053 |
| keywords[9].display_name | Neuroscience |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2402.16288 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2402.16288 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2402.16288 |
| locations[1].id | doi:10.48550/arxiv.2402.16288 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2402.16288 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5101146945 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Yiming Du |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Du, Yiming |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5075179865 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8305-5231 |
| authorships[1].author.display_name | Hongru Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Wang, Hongru |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5111919101 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Zhengyi Zhao |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhao, Zhengyi |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5033058558 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-2795-9997 |
| authorships[3].author.display_name | Bin Liang |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Liang, Bin |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5038563026 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-4858-8937 |
| authorships[4].author.display_name | Baojun Wang |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Wang, Baojun |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5019101763 |
| authorships[5].author.orcid | https://orcid.org/0009-0007-2236-228X |
| authorships[5].author.display_name | Wanjun Zhong |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Zhong, Wanjun |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5100747388 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1061-604X |
| authorships[6].author.display_name | Zezhong Wang |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Wang, Zezhong |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5008208316 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-9427-5659 |
| authorships[7].author.display_name | Kam‐Fai Wong |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Wong, Kam-Fai |
| authorships[7].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2402.16288 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-02-28T00:00:00 |
| display_name | PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9973000288009644 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Topic Modeling |
| related_works | https://openalex.org/W2384605597, https://openalex.org/W2387743295, https://openalex.org/W2115758952, https://openalex.org/W3082787378, https://openalex.org/W2136007095, https://openalex.org/W2366230879, https://openalex.org/W3208425359, https://openalex.org/W2724703943, https://openalex.org/W2963503898, https://openalex.org/W1494049244 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2402.16288 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2402.16288 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2402.16288 |
| primary_location.id | pmh:oai:arXiv.org:2402.16288 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2402.16288 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2402.16288 |
| publication_date | 2024-02-26 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 3, 74, 101 |
| abstract_inverted_index.30 | 81 |
| abstract_inverted_index.QA | 29, 65, 163 |
| abstract_inverted_index.We | 121 |
| abstract_inverted_index.an | 27 |
| abstract_inverted_index.as | 142 |
| abstract_inverted_index.in | 6, 21, 63, 91, 146, 161 |
| abstract_inverted_index.is | 48 |
| abstract_inverted_index.of | 54, 71, 77, 88, 110, 157 |
| abstract_inverted_index.on | 58, 97 |
| abstract_inverted_index.to | 50 |
| abstract_inverted_index.we | 99 |
| abstract_inverted_index.Our | 23 |
| abstract_inverted_index.and | 19, 34, 44, 61, 73, 86, 107, 118, 128, 144 |
| abstract_inverted_index.can | 12 |
| abstract_inverted_index.for | 80, 104 |
| abstract_inverted_index.our | 152 |
| abstract_inverted_index.the | 52, 64, 84, 147, 155, 162 |
| abstract_inverted_index.two | 69 |
| abstract_inverted_index.use | 53 |
| abstract_inverted_index.LLMs | 127, 140 |
| abstract_inverted_index.This | 46 |
| abstract_inverted_index.five | 126 |
| abstract_inverted_index.main | 112 |
| abstract_inverted_index.role | 5 |
| abstract_inverted_index.such | 141 |
| abstract_inverted_index.that | 31, 134 |
| abstract_inverted_index.this | 123 |
| abstract_inverted_index.8,593 | 78 |
| abstract_inverted_index.Based | 96 |
| abstract_inverted_index.Large | 92 |
| abstract_inverted_index.novel | 102 |
| abstract_inverted_index.plays | 2 |
| abstract_inverted_index.study | 153 |
| abstract_inverted_index.task. | 66, 150, 164 |
| abstract_inverted_index.three | 111, 129 |
| abstract_inverted_index.types | 70 |
| abstract_inverted_index.using | 125 |
| abstract_inverted_index.world | 15, 38 |
| abstract_inverted_index.Memory | 114, 116, 119 |
| abstract_inverted_index.Models | 94 |
| abstract_inverted_index.better | 13 |
| abstract_inverted_index.events | 62 |
| abstract_inverted_index.memory | 1, 11, 72, 105, 148, 159 |
| abstract_inverted_index.models | 137 |
| abstract_inverted_index.social | 41, 59 |
| abstract_inverted_index.(LLMs). | 95 |
| abstract_inverted_index.ChatGPT | 145 |
| abstract_inverted_index.PerLTQA | 67 |
| abstract_inverted_index.dataset | 30, 47 |
| abstract_inverted_index.events, | 43 |
| abstract_inverted_index.propose | 100 |
| abstract_inverted_index.results | 132 |
| abstract_inverted_index.ChatGLM3 | 143 |
| abstract_inverted_index.Language | 93 |
| abstract_inverted_index.PerLTQA, | 26, 98 |
| abstract_inverted_index.combines | 32 |
| abstract_inverted_index.critical | 4 |
| abstract_inverted_index.episodic | 35 |
| abstract_inverted_index.evaluate | 122 |
| abstract_inverted_index.features | 68 |
| abstract_inverted_index.focusing | 57 |
| abstract_inverted_index.leverage | 14 |
| abstract_inverted_index.memories | 90 |
| abstract_inverted_index.personal | 7 |
| abstract_inverted_index.research | 24 |
| abstract_inverted_index.semantic | 33 |
| abstract_inverted_index.Long-term | 0 |
| abstract_inverted_index.benchmark | 76 |
| abstract_inverted_index.collected | 49 |
| abstract_inverted_index.effective | 158 |
| abstract_inverted_index.framework | 103, 124 |
| abstract_inverted_index.including | 37 |
| abstract_inverted_index.long-term | 10 |
| abstract_inverted_index.memories, | 36, 56 |
| abstract_inverted_index.profiles, | 40 |
| abstract_inverted_index.questions | 79 |
| abstract_inverted_index.BERT-based | 135 |
| abstract_inverted_index.Retrieval, | 117 |
| abstract_inverted_index.Synthesis. | 120 |
| abstract_inverted_index.consisting | 109 |
| abstract_inverted_index.dialogues. | 22, 45 |
| abstract_inverted_index.highlights | 154 |
| abstract_inverted_index.historical | 17 |
| abstract_inverted_index.importance | 156 |
| abstract_inverted_index.innovative | 28 |
| abstract_inverted_index.introduces | 25 |
| abstract_inverted_index.knowledge, | 16, 39 |
| abstract_inverted_index.outperform | 139 |
| abstract_inverted_index.application | 87 |
| abstract_inverted_index.characters, | 82 |
| abstract_inverted_index.components: | 113 |
| abstract_inverted_index.considering | 9 |
| abstract_inverted_index.demonstrate | 133 |
| abstract_inverted_index.exploration | 85 |
| abstract_inverted_index.generation, | 108 |
| abstract_inverted_index.integration | 106, 160 |
| abstract_inverted_index.investigate | 51 |
| abstract_inverted_index.preferences | 20 |
| abstract_inverted_index.retrievers. | 130 |
| abstract_inverted_index.Experimental | 131 |
| abstract_inverted_index.Furthermore, | 151 |
| abstract_inverted_index.facilitating | 83 |
| abstract_inverted_index.information, | 18 |
| abstract_inverted_index.interaction, | 8 |
| abstract_inverted_index.interactions | 60 |
| abstract_inverted_index.personalized | 55, 89 |
| abstract_inverted_index.comprehensive | 75 |
| abstract_inverted_index.significantly | 138 |
| abstract_inverted_index.classification | 136, 149 |
| abstract_inverted_index.relationships, | 42 |
| abstract_inverted_index.Classification, | 115 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |