大语言模型在表格任务中的应用 On the Use of Large Language Models for Table Tasks

1

OntheUseofLargeLanguageModels

forTableTasks

-Introduction

HaochenZhangChuanXiaoYuyangDong

Tabulardataiseverywhere

(a)Relationaldatabases

(b)Richdocuments,PDF

(c)webpages(d)spreadsheet2

Growingresearchfocus

•GrowquicklyinDB,AIandNLPcommunities

resultfrom[4]

•Recenttutorials

•[1]Webtableextraction,retrievalandaugmentation,SIGIR19

•[2]FromTablestoKnowledge:RecentAdvancesinTableUnderstanding,KDD21

•[3]TransformersforTabularDataRepresentation:AtutorialonModelsandApplicationsVLDB22,SIGMOD23

•[4]LargeLanguageModelsforTabularData:ProgressesandFutureDirections,SIGIR24

•A-Paper-List-of-Awesome-Tabular-LLMs，

/SpursGoZmy/Awesome-Tabular-LLMs

Tabletasks&benchmarks

•TableInterpretation

•EntityLinking

•ColumnTypeAnnotation

•RelationExtraction

•Tabledetection

“Preparetables”

Tablereprocessing

•Tablematching

•Entitymatching

•Schemamatching

•Tablecleaning

•Errordetection

•Dataimputation

•Tableaugmentation

•Rowpopulation

•Schemaaugmentation

•Tablesearch

•Tabletransformation

Tableunderstanding

“Understandtables”“Getanswerfromtables”

Tableanalysis

•TableQA

•Tablefactverification

•Table-to-text

•Text-to-SQL

4

5

Table

preprocessing

“Matchingtworows”

Schemamatching

“Matchingtwocolumns”

Tablematching

matching

Entity

AAA

BBB

CCC

EEE

AAA’

id

name

rev

1

IBMCorp

$57B

2

AppleInc

$366B

3

GE

$74B

DDD

id

name

loc

#of

employee

1

Apple

CA

154,000

2

IBM

NY

282,000

•Datasetandbenchmark

1.CanFoundationModelsWrangleYourData?[VLDB23].

/abs/2205.09911

2.Jellyfish:Instruction-TuningLocalLargeLanguageModelsforDataPreprocessing[EMNLP24]

/abs/2312.01678

ID

Name

DateofBirth

Prefecture

PostalCode

Height

1

Yuka

2003/02/26

Hokkaido

540-8570

165

2

Nana

Aichi

464-0804

157

3

Miho

2001/06/25

Kangawa

2208799

1.60

Table

preprocessing

“Detecttheerrorcells”

Errordetection

inconsistency

missing

typoformatoutlier

“Imputevaluesintocells”

Dataimputation

ID

Name

DateofBirth

Prefecture

PostalCode

Height

1

Yuka

2003/02/26

Osaka

540-8570

165

2

Nana

2003/03/30

Aichi

464-0804

157

3

Miho

2001/06/25

Kanagawa

220-8799

160

Tablecleaning

•Datasetandbenchmark

1.CanFoundationModelsWrangleYourData?[VLDB23].

/abs/2205.09911

6

2.Jellyfish:Instruction-TuningLocalLargeLanguageModelsforDataPreprocessing[EMNLP24]

/abs/2312.01678

7

Table

preprocessing

Tableaugmentation

“Addcolumns/rowstotable”

Column

population

id

name

loc

#of

employee

1

Apple

CA

154,000

2

IBM

NY

282,000

Rowpopulation

•Datasetandbenchmark

1.TURL:TableUnderstandingthroughRepresentationLearning.[VLDB20]

/abs/2006.14806

2.TableLlama:TowardsOpenLargeGeneralistModelsforTables.[NAACL24].

https://osu-nlp-group.github.io/TableLlama/

Tablesearch

Table

preprocessing

“RetrievetableswithanNLquery”

“Tablescontains

informationofAppleInc.”

Search

Tables,

Datalakes,Documents

Tablecollection

id

name

loc

#of

employee

1

Apple

CA

154,000

2

IBM

NY

282,000

•Datasetandbenchmark

1.Open-DomainTableRetrievalforNaturalQuestions.

/zorazrw/nqt-retrieval

2.Open-WikiTable:DatasetforOpenDomainQuestionAnsweringwithComplexReasoningoverTable[EMNLP23]

/sean0042/Open_WikiTable

8

9

Table

preprocessing

Tabletransformation

“Manipulatetableintowantedstyles”

•Datasetandbenchmark

1.SpreadsheetBench:TowardsChallengingRealWorldSpreadsheetManipulation[NeurIPS24]

https://spreadsheetbench.github.io/

Table

understanding

TableInterpretation“classifycolumnsinto

Companydefinedtypes”

1

2

loc

#of

employee

CA

154,000

NY

282,000

id

/wiki/Apple_Inc

Entitylinking

“Extractandpredictrelation

betweentwocolumns”

RelationExtraction

“Matchentitytoknowledgebase”

!Columntypeannotation

name

AppleIBM

anization.headquarters-location

•Datasetandbenchmark

1.TURL:TableUnderstandingthroughRepresentationLearning.[VLDB20]

/abs/2006.14806

2.TableLlama:TowardsOpenLargeGeneralistModelsforTables.[NAACL24].

https://osu-nlp-group.github.io/TableLlama/

3.ColumnTypeAnnotationusingChatGPT[arixv24]

/abs/2306.0071405

Tabledetection

“detecttableregion,structureandcontent”

•Datasetandbenchmark

1.PubTables-1M:Towardscomprehensivetableextractionfromunstructured.[CVPR22]

/microsoft/table-transformer

2.TableFormer:TableStructureUnderstandingwithTransformers.[CVPR22].

/IBM/TabFormer

3.DoclingTechnicalReport.[arxiv24]

/DS4SD/docling-ibm-models

Table

understanding

11

TableQA

Tableanalysis

“Question-answeringontabularcontents”

id

name

loc

#of

employee

1

Apple

CA

154,000

2

IBM

NY

282,000

Question1:WhereisthelocationofIBM?

Answer1:NewYork

Question2:WhatisthesumofemployeeinAppleandIBM?

Answer2:436,000

•Datasetandbenchmark

1.TableLlama:TowardsOpenLargeGeneralistModelsforTables.[NAACL24].

https://osu-nlp-

group.github.io/TableLlama/

12

2.https://huggingface.co/datasets/SpursgoZmy/IFT-Data-For-Tabular-Tasks

Tableanalysis

“Verifyagivensentencesaccordingtotable”

Tablefactverification

id

name

loc

#of

employee

1

Apple

CA

154,000

2

IBM

NY

282,000

Entailed

“IBMandAppleareU.S.companies.”

Refuted

“ApplehasmoreemployeethanIBM.”

•Datasetandbenchmark

1.TabFact:ALarge-scaleDatasetforTable-basedFactVerification.[ICLR20]

https://tabfact.github.io/

13

2.FEVEROUS:FactExtractionandVERificationOverUnstructuredandStructuredinformation.[ACL21workshop].

https://fever.ai/dataset/feverous.html

14

Table-to-text

Tableanalysis

“Generatedescriptionoftable”

“Thetablepresentsinformationabouttwomajor

id

name

loc

#of

employee

1

Apple

CA

154,000

2

IBM

NY

282,000

companies,AppleandIBM,alongwiththeirlocationsandemployeecounts.Apple,headquarteredinCalifornia(CA),employs154,000people.Ontheotherhand,IBM,based

inNewYork(NY),hasasignificantlylargerworkforce,with282,000employees.”

•Datasetandbenchmark

1.NeuralTextGenerationfromStructuredDatawithApplicationtotheBiographyDomain[EMNLP16]

/DavidGrangier/wikipedia-biography-dataset

2.ToTTo:AControlledTable-To-TextGenerationDataset[EMNLP20]

https://huggingface.co/datasets/google-research-datasets/totto

3.Table-to-text:Describingtableregionwithnaturallanguage.[AAAI18]

/msra-

nlc/Table2Text

15

Text-to-SQL

Tableanalysis

Text:HowmanyemployeesinApple?

SELECT`employee_num`FROMtable_name

WHEREname='Apple';

“ConvertnaturallanguagetoSQLquery”

id

name

loc

employee_num

1

Apple

CA

154,000

2

IBM

NY

282,000

•Datasetandbenchmark

1.Seq2SQL:GeneratingStructuredQueriesfromNaturalLanguageusingReinforcementLearning.[ICLR18]

/salesforce/WikiSQL

2.Spider[EMNLP18]

https://yale-lily.github.io/spider

MethodsbeforeLLM

•Rule,ML,NN–based->skip

•Transformer-based(2018-)

•Encoder

•Encoder-Decoder

“GPT”

decoder

“BERT”

encoder

“T5”

16

Transformer(encoder-decoder)

17

MotivationofEncoderfortables

•Pretrain-and-finetune(“BERT-way”)

•Learninggoodtablerepresentation(embedding)withtablepretrainingtasks

•Finetuneondownstreamtasks

Largeunlabelleddata

Pre-training(Encoder)

Smalllabelled

downstreamtaskdata

task1

task2

Fine-tuning

(Additional

layer)

Fine-tuning

(Additional

layer)

Table-onlyPretraining

•Pretrainwithtablecontents

TURL

•TURL[VLDB20]

•MaskedLanguageModel(MLM)

•MaskedEntityRecovery

•TABBIE[NAACL21]

•Detectcorruptedcells

•TUTA[KDD21]

•MLM

•Cellfilling

TABBIE

•Contextselection

TURL:

/pdf/2006.14806

TABBIE:

/abs/2105.02584

TUTA:

/pdf/2010.12537

Table-and-queryPretraining

•Pretrainwithtablecontents&query

•TAPAS[ACL20]

•Queryandwholetable

•Aggregationprediction

•Cellselectiontask

TAPAS

•TaBERT[NAACL21]

•Queryandrelatedrows

•MaskedLanguageModel

TAPAS:

/abs/2004.02349

TaBERT:

/abs/2005.08314

19

TaBERT

20

MotivationofEncoder-decoderfortables

•Flexibleinputandoutput

•Tabletotext

•Texttosql

•Tablesummarize

•Tabletomarkdown,html

•Mulitmodalability

•Imageencoder->textdecoder:tableOCRtask,tableVQA

•Generalizedandgoodgenerationability

Image

Text

HTML

Markdown

Encoder

Decoder

ImageTextHTML

Markdown

Text-to-textEncoderDecoder

•GeneralizedandGoodgenerativeabilitybyfine-tuningonpretrainedencoder

decodermodel

•UnifiedSKG[EMNLP22]

•FinetuneT5

UnifiedSKG

•TaPEx[ICLR22]

•FinetuneBART

UnifiedSKG:

/abs/2201.05966

TaPEx:

/abs/2107.07653

TaPEx

21

VisionEncoderDecoder

•Multimodality

•Tableformer[CVPR22]:Tabledetection&OCR

Tableformer

•Boundingboxdetection,structuregeneration

•TATR[CVPR22]

•Tabledetection

•Basedonobjectdetectiontransformer(DETR)

Tableformer:

/abs/2203.01017

TATR:

/microsoft/table-transformer

TATR(DETR)

22

LLM(decodersfortable)

•AutoregressiveGeneration

•Goodforgenerativetask

•Easyforself-supervisedtraining

•Simplearchitecture

•Efficientontraininglarge-scaledata

•Easytoscalethemodel

“BERT”

encoder

“GPT”

“T5”

23

-

Transformer(encoderdecoder)

decoder

MotivationofLLMfortabletask

•Goodpoints

•Largeknowledge

•Incontextlearning/zeroshotability

•Generationtask,interactive

•Limitation

•Slow

•Cost

•Notgoodperformancefor

•Mathematicaltask

•Largetable

25

Prompt

Fine-tuning

VLM

UseofLLMintabletasks

RAG

LLMagents

26

OntheUseofLargeLanguageModels

forTableTasks

-Prompting

PromptEngineering

BadPromptGoodPrompt

I'mhungry.

I'manadultman

weighing200poundswithadailycaloric

requirementof2500.Ineedtohitmyproteingoalforthedaywhichis120grams.I'm

hungryforsomethinghotandalittlesalty.

Zero-shot

•InstructtheLLMtofollowprompts.

Few-shot

•Demonstratewithoneormoreexamples.

Scrambleandcook4eggs.

Chain-of-thought

•Elicitcomplexreasoningthrougha

seriesofintermediatereasoningsteps.

•Variants:tree-of-thought,graph-of-thought,etc.

Certainly,here'sawholebunchof

uselesstextbecauseI

don'tknowwhatyouwantbecauseIcan'treadyourmind

becauseallIdois

generatetextfrom

prompts.Ifyouare

unsatisfiedwithmy

responseplease

provideamoreusefulprompt.

27

source:

/the-augmented-engineer/new-to-ai-prompt

-writing-learn-how-to-get-summaries-that-cover-all-the-key-points-you-care-about-46615fab135b

Zero-ShotPrompting

•Instructthemodeltofollowspecificprompts.

Iwenttothemarketandbought10apples.Igavetwotomy

neighborandtwototherepairman.ThenIbought5applesandateone.Howmanyapplesareleft?

Let'sthinkaboutthisstepbystep.

First,Ibought10apples.

1.Igavetwoapplestomyneighbor;10-2=8applesleft.

2.Igavetwoapplestotherepairman;8-2=6applesleft.

3.ThenIbought5apples.6+5=11applesleft.

4.Oneappleeaten;11-1=10applesleft.Asaresult,thereare10applesleft.

Kojimaetal.Largelanguagemodelsarezero-shotreasoners.NeurIPS2022.

28

source:

/Jazuma/items/9de7c56fd9fa8b9c1648

Few-ShotPrompting

•Demonstratetask-solvingwithexamplesprovidedin

Fantastic!//PositiveTerrible!//Negative

Themoviewasamazing!//PositiveWhatahorribleshow!//

prompts.

Negative

source:

/Jazuma/items/9de7c56fd9fa8b9c1648

29

Brownetal.Languagemodelsarefew-shotlearners.NeurIPS2020.

Chain-of-Thought

•Elicitcomplexreasoningbyprovidinginferenceprocesses.

Theoddnumbersinthisgroupadduptoanevennumber.:4,8,

9,15,12,2,1.

A:Addingalloddnumbersgives9+15+1=25.Theansweris

False.

Theoddnumbersinthisgroupadduptoanevennumber.:15,32,5,13,82,7,1.

A:

Addingalltheoddnumbersgives15+5+13+7+1=41.TheanswerisFalse.

source:

/Jazuma/items/9de7c56fd9fa8b9c1648

Weietal.Chainofthoughtpromptingelicitsreasoninginlargelanguagemodels.NeurIPS2022.Wangetal.Self-consistencyimproveschainofthoughtreasoninginlanguagemodels.ICLR2023.

30

31

IssuesofPromptingforTableTasks

Task

Variety

(TV)

吁

............

Data

Format

(DF)

............

PDF

............

Data

Volume

(DV)

Task

Complexity

(TC)

32

PromptingTechniquesforTableTasks

☞TaskComplexity

☞DataFormat

☞DataVolume

☞TaskVariety

Task

decomposition

Instanceprompting

Tableencoding

Zero-shotprompting

Table

decomposition

Table

reconstruction

Batch

prompting

Few-shotprompting

Prefixcaching

Chain-of-thought

33

☞TaskVariety

Invariant

Zero-shot

TablePreprocessing

TableUnderstanding

Few-shot

TableAnalysis

Variant

Chain-of-thought(and

variants)

☞TaskVariety:Zero-ShotPrompting

name

city

addr

phone

Type

Langer's

?

704S.Alvarado

St.

213-483-

8050

delis

TaskType

DataImputation

TaskDescription

Youareadatabaseengineer.

Youarerequestedtoinferthevalueofthe"city"attributebasedonthevaluesofotherattributes.

pt

DataInstance

[name:"langer's",addr:"704s.alvaradost.",phone:"213-483-8050",type:

"delis"]

prom

Answer

Thecityis"LosAngeles".

Zhangetal.Largelanguagemodelsasdatapreprocessors.TaDA2024.

Table

Preprocessing

34

☞TaskVariety:Few-ShotPrompting

TaskType

DataImputation

TaskDescription

Youareadatabaseengineer.

Youarerequestedtoinferthevalueofthe"city"attributebasedonthevaluesofotherattributes.

pt

DataInstance

[name:"langer's",addr:"704s.alvaradost.",phone:"213-483-8050",type:"delis"]

Few-shotExamples

Someexamplesaregivenbelow.

```

User:

Question1:Recordis[name:"carey'scorner",addr:"1215powersferryrd.",phone:"770-933-0909",type:"hamburgers"].Whatisthecity?

Assistant:

Answer1:Marietta

…prom

```

Answer

LosAngeles

Zhangetal.Largelanguagemodelsasdatapreprocessors.TaDA2024.

Table

Preprocessing

35

TaskTypeDataImputation

TheaddressandphonenumberpointtoalocationinLosAngeles,CA,

knownforitsdis

tinctareacode(213)and

localbusinesses.

Answer

…

LosAngeles

☞TaskVariety:Chain-of-Thought

TaskDescription

…

MUSTanswereachquestionintwolines.Inthefirstline,yougivethe

reasonfortheinference.Inthesecondline,youONLYgivethevalueofthe"city"attribute.

pt

DataInstance

[name:"langer's",addr:"704s.alvaradost.",phone:"213-483-8050",type:"delis"]

Few-shotExamples

Answer1:Thephoneareacode770correspondstotheAtlanta

metropolitanareainGeorgia,and"1215PowersFerryRd."isanaddresslocatedinMarietta,Georgia;therefore,thecityisMarietta.

Mariettaprom

Someexamplesaregivenbelow.

```

User:

Question1:Recordis[name:"carey'scorner",addr:"1215powersferryrd.",phone:"770-933-0909",type:"hamburgers"].Whatisthecity?

Assistant:

36

Zhangetal.Largelanguagemodelsasdatapreprocessors.TaDA2024.

Table

Preprocessing

☞TaskVariety:

Chain-of-Thought(+Self-ConsistencyDecoding)

TaskTypeDataImputation

Answer

Thenamereferstoa

restaurantinthecityofSantaMonica,CA.The

phonenumberalsocorrespondsto

California.

SantaMonica

Answer

Thephonenumber

correspondtoalocationinLosAngeles,

California,recognizedforitsunique213areacode.

LosAngeles

Weightedsum/majority

voting

•Generatemultiplereasoninganswersandaggregatethem.

TaskDescription

…

pt

DataInstance

[name:"langer's",addr:"704s.alvaradost.",phone:"213-483-8050",type:"delis"]

Few-shotExamples

…prom

Answer

Theaddressandphone

numberpointtoa

locationinLosAngeles,CA,knownforitsdistinctareacode(213)andlocalbusinesses.

LosAngeles

Answer

LosAngeles

Chen.Largelanguagemodelsarefew(1)-shottablereasoners.EACL2023.

TATable

Chenetal.Programofthoughtsprompting:Disentanglingcomputationfrom

QA

37

reasoningfornumericalreasoningtasks.TMLR2023.

38

•自Task

•Table

☞TaskComplexity

•Taskdecomposition

•Tabledecomposition

Strategies

Sourcesofcomplexity

☞TaskComplexity:TaskDecomposition

Beforedecomposition

?

?

?

?

FriendsPizza

2525

CashVisaMasterCard

7:30AM

TaskType

ColumnTypeAnnotation

TaskDescription

Classifythecolumnsofagiventablewithonlyoneofthefollowingclassesthatareseparatedwithcomma:

descriptionofevent,descriptionofrestaurant,postalcode,regionofaddress…

DataInstance

Column1||Column2||Column3||Column4¥n

FriendsPizza||2525||CashVisaMasterCard||7:30AM¥n

Answer

name,number,payment,time

KoriniandBizer.ColumntypeannotationusingChatGPT.TaDA2023.

TUTableInterpretation

Zhaoetal.Largelanguagemodelsarecomplextableparsers.EMNLP2023.

TATableQA

TPTableTransformation

TATableQA

39

Wangetal.Chain-of-table:Evolvingtablesinthereasoningchainfortableunderstanding.ICLR2024.

TUTableInterpretation

Dongetal.OpenTE:Open-structuretableextractionfromtext.ICCASP2024.

Sub-Task1

Sub-Task2

Yourtaskistoclassifyifatable

describesRestaurants,Events,MusicRecordings,orHotels.

Sub-TaskType

TableClassification

Task

Description

Data

Instance

Column1||Column2||Column3||Column4¥n

FriendsPizza||2525||CashVisaMasterCard||7:30AM¥n

Answer

Restaurant

KoriniandBizer.ColumntypeannotationusingChatGPT.TaDA2023.

Zhaoetal.Largelanguagemodelsarecomplextableparsers.EMNLP2023.

Wangetal.Chain-of-table:Evolvingtablesinthereasoningchainfortableunderstanding.ICLR2024.

Dongetal.OpenTE:Open-structuretableextractionfromtext.ICCASP2024.

TATableQA

40

☞TaskComplexity:TaskDecomposition

Afterdecomposition

Sub-TaskType

ColumnClassification

Task

Description

Yourtaskistoclassifythecolumnsofagiventablewithonlyoneofthe

followingclassesthatareseparatedwithcomma:nameofrestaurant,

descriptionofrestaurant…

Data

Instance

Column1||Column2||Column3||Column4¥n

FriendsPizza||2525||CashVisa

Answer

MasterCard||7:30AM¥

nameofrestaurant,postalcode,

paymentaccepted,time

TUTableInterpretation

TATableQA

TPTableTransformation

TUTableInterpretation

☞TaskComplexity:TableDecomposition

TabIe:FigureskatingattheAsianwinterGames

Rank

Nation

Gold

silver

Bronze

Total

1

China

13

9

13

35

2

Japan

7

10

7

24

3

uzbekistan

1

2

3

6

4

kazakhstan

2

2

0

4

5

Northkorea

1

0

1

2

6

southkorea

0

0

2

2

TotaI

24

23

26

73

Q:whoreceivedmorebronzemedaIs:japanorsouthkorea?

A:Japan

TabIetitIe:FigureskatingattheAsianwinterGames

CoIumns:['rank','nation','goId','siIver','bronze','totaI']

Q:whoreceivedmorebronzemedaIs:japanorsouthkorea?

sub-tabIe

Nation

Bronze

Japan

7

southkorea

2

sub-tabIeseIection

(LLM)

Execute

sQL:seIectnation,bronzefromT

wherenation='japan'ornation='southkorea'

(1)subtabIeseIection

sub-tabIe

TabIetitIe:FigureskatingattheAsianwinterGames

Nation

Bronze

Japan

7

southkorea

2

seIectnation,bronzefromT

wherenation='japan'ornation='southkorea'

Q:whoreceivedmorebronzemedaIs:japanorsouthkorea?

AnswerGeneration

(LLM)

Response:BasedonthetabIe,Japanreceived7bronzemedaIsandsouthkoreareceived2bronzemedaIs.Therefore,Japanreceived

morebronzemedaIsthansouthkorea.

Answer:Japan

(2)ReasoningandAnswerGeneration

TATableQA

TAText-to-SQL

NahidandRafiei.TabSQLify:EnhancingreasoningcapabilitiesofLLMsthroughtabledecomposition.NAACL2024.

TATableQA

Patnaiketal.Cabinet:Contentrelevancebasednoisereductionfortablequestion

answering.ICLR2024.

TATableQA

TAText-to-SQL

Jiangetal.StructGPT:Ageneralframeworkforlargelanguagemodeltoreasonoverstructureddata.EMNLP2023.

☞TaskComplexity:

Text

transformation

Question

ReportthenumberofwinsinGrandSlam

tournaments.

tournament

…

attn

career_w/l

AustralianOpen

…

18

RolandGarros

…

14

Wimbledon

…

18

USOpen

…

13

IndianWells

…

15

tournament

…

attn

AustralianOpen

…

18

RolandGarros

…

14

Wimbledon

…

18

USOpen

…

13

IndianWells

…

15

career_w/l

22-18

11-14

13-18

16-13

20-15

n_win

22

11

13

16

…………

…………

…………

13-18

11-14

22-18

16-13

20-15

tournament

…

attn

career_w/l

AustralianOpen

…

18

22-18

RolandGarros

…

14

11-14

Wimbledon

…

18

13-18

USOpen

…

13

16-13

IndianWells

…

15

20-15

TableDecomposition(ProgressivePrompting)

Focusoncolumnselection.

Incorporatebothcolumnandrowselection.

Applyadditionaloperations(e.g.,aggregationfunctionsandtext

operations).

Setthegroundworkfor

understandinghowtofetch

specificdatafromadatabase.

Extractparticularcolumnsandfilteringrowsbasedonspecifiedcriteria,enhancingprecisionindatagathering.

Aggregationfunctionsempowerdatasummarization.

Textoperationsfacilitatethe

manipulationandtransformationofstringdata.

42

reasoners.ICLR2024.

Kongetal.OpenTab:Advancinglargelanguagemodelsasopen-domaintable

TAText-to-SQL

43

☞DataFormat:TableEncoding

text

(serialized)

spreadsheet

markup

key-value

program

image

embedded

easyhard

☞DataFormat:TableReconstruction

Zhaoetal.Largelanguagemodelsarecomplextableparsers.EMNLP2023.

TATableQA

44

☞DataVolume:InstancePrompting

name

city

addr

phone

Type

Langer's

?

704S.AlvaradoSt.

213-483-8050

delis

Valetino

?

3115PicoBlvd.

310-829-4313

Italian

CafeBizou

?

14016VenturaBlvd.

818/788-3536

French

Youareadatabaseengineer.

Youarerequestedtoinferthevalueofthe"city"

attributebasedonthe

valuesofotherattributes.

[name:"langer's",addr:

"704s.alvaradost.",phone:"213-483-8050",type:

"delis"]

Task

Description

Data

Instance

TaskType

DataImputation

TaskType

DataImputation

TaskType

DataImputation

prompt

prompt

Youareadatabaseengineer.

Youarerequestedtoinferthevalueofthe"city"

attributebasedonthe

valuesofotherattributes.

[name:"cafebizou",addr:"14016venturablvd.",

phone:"818/788-3536",

Task

Description

Data

Instance

type:"french"]prompt

Youareadatabaseengineer.

Youarerequestedtoinferthevalueofthe"city"

attributebasedonthe

valuesofotherattributes.

[name:"valentino",addr:"3115picoblvd.",phone:"310-829-4313",type:

"italian"]

Task

Description

Data

Instance

Zhangetal.Large

languagemodelsasdata

TablePreprocessing

preprocessors.TaDA2024.

standardprompting

#K-shotin-conte×te×empars

Q:{question}

A:{answer}

Q:{question}

A:{answer}

#onesampetoinference

Q:A1ihad$21.Lei1agavehimha1fofher$1.HowmuchdoesA1ihavenow?

#Response

A:Lei1agave1/2=5toA1i.A1inowhas $21+$5=$71.Theansweris71.

Batchprompting

#K-shotin-conte×te×emparsinK/bbatches

Q[1]:{question}Q[2]:{question}A[1]:{answer}

A[2]:{answer}

#bsampesinabatchtoinference

Q[1]:A1ihad$21.Lei1agavehimha1fofher

$1.HowmuchdoesA1ihavenow?

Q[2]:Arobetakes2bo1tsofb1uefiberandha1fthatwhitefiber.Howmanybo1ts?

b(=2)samplesinonebatch

#Responsestoabatch

A[1]:Lei1agave1/2=5toA1i.A1inowhas

$21+$5=$71.Theansweris71.

A[2]:Ittakes2/2=1bo1tofwhitefiber.The tota1amountis2+1=3.Theansweris3.

source:Chengetal.Batchprompting:EfficientinferencewithlargelanguagemodelAPIs.EMNLP2023.

☞DataVolume:BatchPrompting

name

city

addr

phone

Type

Langer's

?

704S.AlvaradoSt.

213-483-8050

delis

Valetino

?

3115PicoBlvd.

310-829-4313

Italian

CafeBizou

?

14016VenturaBlvd.

818/788-3536

French

TaskType

DataImputation

Task

Description

Youareadatabaseengineer.

Youarerequestedtoinferthevalueofthe"city"attributebasedonthevaluesofotherattributes.

Data

Instance

[name:"langer's",addr:"704s.alvaradost.",phone:"213-483-8050",type:"delis"]

[name:"valentino",addr:"3115picoblvd.",phone:"310-829-4313",type:"italian"]

[name:"cafebizou",addr:"14016venturablvd.",phone:"818/788-3536",type:"french"]prompt

Table46

Preprocessing

Zhangetal.Largelanguagemodelsasdatapreprocessors.TaDA2024.

☞DataVolume:

PrefixCaching(+InstancePrompting)

Youarerequestedtoinferthevalueofthe"city"

attributebasedonthe

valuesofotherattributes.

Youarerequestedtoinferthevalueofthe"city"

attributebasedonthe

valuesofotherattributes.

Youareadatabase

engineer.

[name:"cafebizou",addr:

"14016venturablvd.",

phone:"818/788-35o",mpt

"

Youarerequestedtoinferthevalueofthe"city"

attributebasedonthe

valuesofotherattributes.

Youareadatabase

engineer.

[name:"langer's",addr:

"704s.alvaradost.",phone:"213-483-8050",typ:rompt

Task

Description

Data

Instance

"delis"]

Task

Description

Data

Instance

type:french"]

•UseAutomaticPrefixCaching(APC)inthevLLMlibrary.

Task

Description

Data

Instance

Youareadatabase

engineer.

[name:"valentino",addr:

"3115picoblvd.",phone:

"310-829-4313",typ:rompt

"

italian"]

KVcache:

cachekeyvectorstospeedupQKV

attentionin

Transformers

Shareprefix

ShareKV