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KITAB: EVALUATING LLMS ON CONSTRAINT SATISFACTION FOR INFORMATION RETRIEVAL
Marah I. Abdin
, Suriya Gunasekar
,
Varun Chandrasekaran
, Jerry Li
, Mert Yuksekgonul
, Rahee Ghosh Peshawaria
, Ranjita Naik
, Besmira Nushi
Electrical and Computer Engineering
Siebel School of Computing and Data Science
Information Trust Institute
Coordinated Science Lab
Research output
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Contribution to conference
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Paper
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peer-review
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Dive into the research topics of 'KITAB: EVALUATING LLMS ON CONSTRAINT SATISFACTION FOR INFORMATION RETRIEVAL'. Together they form a unique fingerprint.
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Keyphrases
Information Retrieval
100%
Constraint Satisfaction
100%
Irrelevant Information
33%
Context Availability
33%
Large Language Models
33%
Information Popularity
33%
Data Constraints
16%
Knowledge Base
16%
Context Model
16%
Failure Mode
16%
San Diego
16%
Similarity Test
16%
Web Search
16%
Hallucinations
16%
Ice Cream
16%
New Dataset
16%
Language Model
16%
State-of-the-art Models
16%
Verification Approaches
16%
Current Retrieval
16%
Retrieval Benchmark
16%
Web Knowledge
16%
GPT-3.5
16%
GPT-4
16%
Constraint Validation
16%
Dynamic Data Collection
16%
Computer Science
Constraint Satisfaction Problems
100%
Information Retrieval
100%
Large Language Model
33%
Open Source
16%
Data Constraint
16%
World Wide Web Search
16%
Type Constraint
16%
Language Modeling
16%
Knowledge Base
16%
Answer Constraint
16%