Concept-aware Data Construction Improves In-context Learning of
Language Models

k 2024

Concept-aware Data Construction Improves In-context Learning of Language Models

ŠTEFÁNIK, Michal, Marek KADLČÍK and Petr SOJKA

Basic information

Original name

Concept-aware Data Construction Improves In-context Learning of Language Models

Authors

ŠTEFÁNIK, Michal (703 Slovakia, guarantor, belonging to the institution), Marek KADLČÍK (203 Czech Republic, belonging to the institution) and Petr SOJKA (203 Czech Republic, belonging to the institution)

Edition

ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models, 2024

Other information

Language

English

Type of outcome

Prezentace na konferencích

Field of Study

10302 Condensed matter physics

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Paper summary Openreview preprint

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.48550/arXiv.2403.09703

Keywords (in Czech)

velké jazykové modely; LLM; učení v kontextu; učení konceptů; trénování z užitím konceptů; CoAT

Keywords in English

language models; LLM; in-context learning; Concept-aware Training; CoAT

Abstract

V originále

Many recent language models (LMs) of the Transformers family are capable of in-context learning (ICL), manifested in the LMs' ability to perform a new task solely from its description in a natural language input. Previous work curating these models assumes that ICL emerges from vast over-parametrization or the scale of multi-task training, but recent theoretical work attributes ICL emergence to training data properties, creating in-context learners with small, synthetic data. Inspired by these findings, we propose Concept-aware Training (CoAT), a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. We find that by using CoAT, pre-trained transformers can learn to better utilise new latent concepts from demonstrations and that such ability makes ICL more robust to functional deficiencies of the previous models. Finally, we show that concept-aware in-context learning improves ICL performance on a majority of new tasks compared to traditional instruction tuning, reaching performance comparable to the multitask learners using magnitudes of more training data.

Links

MUNI/A/1590/2023, interní kód MU

Name: Využití technik umělé inteligence pro zpracování dat, komplexní analýzy a vizualizaci rozsáhlých dat

Investor: Masaryk University, Using artificial intelligence techniques for data processing, complex analysis and visualization of large-scale data

Citovat

ŠTEFÁNIK, Michal, Marek KADLČÍK and Petr SOJKA. Concept-aware Data Construction Improves In-context Learning of Language Models. In ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models. 2024. Available from: https://dx.doi.org/10.48550/arXiv.2403.09703.

@proceedings{2403040,
   author = {Štefánik, Michal and Kadlčík, Marek and Sojka, Petr},
   booktitle = {ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models},
   doi = {http://dx.doi.org/10.48550/arXiv.2403.09703},
   keywords = {language models; LLM; in-context learning; Concept-aware Training; CoAT},
   language = {eng},
   title = {Concept-aware Data Construction Improves In-context Learning of Language Models},
   url = {https://linnk.ai/insight/language-models/concept-aware-data-construction-enhances-language-model-learning-O6PLTurH/},
   year = {2024}
}

TY  - CONF
ID  - 2403040
AU  - Štefánik, Michal - Kadlčík, Marek - Sojka, Petr
PY  - 2024
TI  - Concept-aware Data Construction Improves In-context Learning of Language Models
KW  - language models
KW  - LLM
KW  - in-context learning
KW  - Concept-aware Training
KW  - CoAT
UR  - https://linnk.ai/insight/language-models/concept-aware-data-construction-enhances-language-model-learning-O6PLTurH/
N2  - Many recent language models (LMs) of the Transformers family are capable of in-context learning (ICL), manifested in the LMs' ability to perform a new task solely from its description in a natural language input. Previous work curating these models assumes that ICL emerges from vast over-parametrization or the scale of multi-task training, but recent theoretical work attributes ICL emergence to training data properties, creating in-context learners with small, synthetic data. Inspired by these findings, we propose Concept-aware Training (CoAT), a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. We find that by using CoAT, pre-trained transformers can learn to better utilise new latent concepts from demonstrations and that such ability makes ICL more robust to functional deficiencies of the previous models. Finally, we show that concept-aware in-context learning improves ICL performance on a majority of new tasks compared to traditional instruction tuning, reaching performance comparable to the multitask learners using magnitudes of more training data.
ER  -

ŠTEFÁNIK, Michal, Marek KADLČÍK and Petr SOJKA. Concept-aware Data Construction Improves In-context Learning of Language Models. In \textit{ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models}. 2024. Available from: https://dx.doi.org/10.48550/arXiv.2403.09703.

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