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Learning from Task Descriptions paper by Independent / Community
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Paper

Learning from Task Descriptions

by Independent / Community 016ca039d9f5220c96b26f15d90d82064c361bfa
Free2AITools Nexus Index
70.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 86
P: Popularity 63
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this framework with a new English language dataset, ZEST, structured for task-oriented evaluation on unse...

Source β†’
Semantic Scholar 96 Citations
Paper Information Summary
Entity Passport
Registry ID 016ca039d9f5220c96b26f15d90d82064c361bfa
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{016ca039d9f5220c96b26f15d90d82064c361bfa,
  author = {Unknown},
  title = {Learning from Task Descriptions Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/016ca039d9f5220c96b26f15d90d82064c361bfa}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Learning from Task Descriptions [Paper]. Free2AITools. https://api.semanticscholar.org/016ca039d9f5220c96b26f15d90d82064c361bfa

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 86
Popularity (P) 63
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Learning from Task Descriptions: Authority (A:86), Popularity (P:63), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this framework with a new English language dataset, ZEST, structured for task-oriented evaluation on unse..."

❝ Cite Node 

@article{Unknown2026Learning,
  title={Learning from Task Descriptions},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ96CitationsSemantic Scholar
πŸ›οΈ86AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈtext generationField

🏷️ Research Topics

instruction tuning
πŸ“¦Data Source: semantic_scholar
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Open Metadata

πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
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citations
96

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