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

• URL: http://arxiv.org/abs/2403.09703v2
• Date: Fri, 28 Jun 2024 08:03:19 GMT
• Title: Concept-aware Data Construction Improves In-context Learning of Language Models
• Authors: Michal Štefánik, Marek Kadlčík, Petr Sojka,
• Abstract summary: We show that concept-aware in-context learning is more effective for a majority of new tasks when compared to traditional instruction tuning. 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.
• Score: 2.4715271879679395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many recent language models (LMs) are capable of in-context learning (ICL), manifested in the LMs' ability to perform a new task solely from natural-language instruction. Previous work curating in-context learners assumes that ICL emerges from a vast over-parametrization or the scale of multi-task training. However, recent theoretical work attributes the ICL ability to concept-dependent training data and creates functional in-context learners even in small-scale, synthetic settings. In this work, we practically explore this newly identified axis of ICL quality. 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 the functional deficiencies of the previous models. Finally, we show that concept-aware in-context learning is more effective for a majority of new tasks when compared to traditional instruction tuning, resulting in a performance comparable to the previous in-context learners using magnitudes of more training data.

Related papers

• Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning [99.05401042153214]
  In-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) and task learning (TL) We take the first step by examining the pre-training dynamics of the emergence of ICL. We propose a simple yet effective method to better integrate these two abilities for ICL at inference time.
  arXiv  Detail & Related papers  (2024-06-20T06:37:47Z)
• DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning [75.68193159293425]
  In-context learning (ICL) allows transformer-based language models to learn a specific task with a few "task demonstrations" without updating their parameters. We propose an influence function-based attribution technique, DETAIL, that addresses the specific characteristics of ICL. We experimentally prove the wide applicability of DETAIL by showing our attribution scores obtained on white-box models are transferable to black-box models in improving model performance.
  arXiv  Detail & Related papers  (2024-05-22T15:52:52Z)
• Towards More Unified In-context Visual Understanding [74.55332581979292]
  We present a new ICL framework for visual understanding with multi-modal output enabled. First, we quantize and embed both text and visual prompt into a unified representational space. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
  arXiv  Detail & Related papers  (2023-12-05T06:02:21Z)
• In-Context Exemplars as Clues to Retrieving from Large Associative Memory [1.2952137350423816]
  In-context learning (ICL) enables large language models (LLMs) to learn patterns from in-context exemplars without training. How to choose exemplars remains unclear due to the lack of understanding of how in-context learning works. Our study sheds new light on the mechanism of ICL by connecting it to memory retrieval.
  arXiv  Detail & Related papers  (2023-11-06T20:13:29Z)
• Link-Context Learning for Multimodal LLMs [40.923816691928536]
  Link-context learning (LCL) emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs. LCL guides the model to discern not only the analogy but also the underlying causal associations between data points. To facilitate the evaluation of this novel approach, we introduce the ISEKAI dataset.
  arXiv  Detail & Related papers  (2023-08-15T17:33:24Z)
• SINC: Self-Supervised In-Context Learning for Vision-Language Tasks [64.44336003123102]
  We propose a framework to enable in-context learning in large language models. A meta-model can learn on self-supervised prompts consisting of tailored demonstrations. Experiments show that SINC outperforms gradient-based methods in various vision-language tasks.
  arXiv  Detail & Related papers  (2023-07-15T08:33:08Z)
• Concept-aware Training Improves In-context Learning Ability of Language Models [0.0]
  Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability. We propose a method to create LMs able to better utilize the in-context information. We measure that data sampling of Concept-aware Training consistently improves models' reasoning ability.
  arXiv  Detail & Related papers  (2023-05-23T07:44:52Z)
• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• Resources and Few-shot Learners for In-context Learning in Slavic Languages [0.22940141855172028]
  We collect the infrastructure necessary for training and evaluation of in-context learning (ICL) in Slavic languages. We evaluate a set of the most recent in-context learners and compare their results to the supervised baselines. We find that ICL models tuned in English are also able to learn some tasks from non-English contexts.
  arXiv  Detail & Related papers  (2023-04-04T16:16:25Z)
• The Learnability of In-Context Learning [16.182561312622315]
  We propose a first-of-its-kind PAC based framework for in-context learnability. Our framework includes an initial pretraining phase, which fits a function to the pretraining distribution. We show that in-context learning is more about identifying the task than about learning it.
  arXiv  Detail & Related papers  (2023-03-14T13:28:39Z)
• In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models [55.78264509270503]
  We introduce in-context learning distillation to transfer in-context few-shot learning ability from large models to smaller models. We perform in-context learning distillation under two different few-shot learning paradigms: Meta In-context Tuning (Meta-ICT) and Multitask In-context Tuning (Multitask-ICT) Our experiments and analysis reveal that in-context learning objectives and language modeling objectives are complementary under the Multitask-ICT paradigm.
  arXiv  Detail & Related papers  (2022-12-20T22:11:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.