Pre-Training to Learn in Context (2305.09137v1)

Abstract: In-context learning, where pre-trained LLMs learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited because LLMs are not explicitly trained to learn in context. To this end, we propose PICL (Pre-training for In-Context Learning), a framework to enhance the LLMs' in-context learning ability by pre-training the model on a large collection of "intrinsic tasks" in the general plain-text corpus using the simple LLMing objective. PICL encourages the model to infer and perform tasks by conditioning on the contexts while maintaining task generalization of pre-trained models. We evaluate the in-context learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark, which contains 100+ NLP tasks formulated to text generation. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger LLMs with nearly 4x parameters. The code is publicly available at https://github.com/thu-coai/PICL.

An Overview of "Pre-Training to Learn in Context"

The paper "Pre-Training to Learn in Context" introduces a novel framework, PICL (Pre-training for In-Context Learning), which seeks to enhance the in-context learning (ICL) ability of pre-trained LLMs (PLMs) by leveraging intrinsic tasks from a large-scale general corpus. In-context learning refers to the capability of LLMs to perform new tasks by conditioning on examples and instructions provided within their input context. This characteristic is significant as it allows the PLMs to adapt to new tasks without additional parameter tuning, potentially bringing us closer to more general forms of artificial intelligence.

Methodology and Framework

The proposed methodology centers around exploiting unlabeled textual corpora to identify intrinsic tasks present across various paragraphs. These tasks are not manually annotated but are inherent within the text, offering broader task diversity and reduced bias compared to dataset-specific fine-tuning methods.

The core process involves the following steps:

1. Intrinsic Task Identification: Paragraphs in a document are assessed for inherent tasks using a task-semantics encoder trained via contrastive learning. This encoder facilitates grouping of paragraphs that imply similar tasks.
2. Data Construction: Once the paragraphs are semantically grouped, these are concatenated into sequences, forming what the authors term "pre-training instances". This setup essentially mirrors the format of in-context learning during evaluation, thereby aligning the training process with the intended application of the PLM.
3. Pre-Training with LLMing: The model is then pre-trained using an objective that combines in-context learning with a traditional LLMing task. This dual objective helps retain the general language capabilities while bolstering the in-context learning feature.

Evaluation and Results

The effectiveness of the PICL framework is validated across multiple datasets:

• Text Classification Tasks: PICL is tested on seven commonly used datasets, demonstrating significant improvements over baseline models such as VanillaICL and MetaICL. The framework notably allows a 770M parameter model to perform on par or better than a 2.7B parameter model that did not utilize the PICL framework.
• Super-NaturalInstructions Benchmark: This benchmark, encompassing over 100 text-generation tasks, further substantiates the model's generalization capabilities. PICL outperformed competing models like MetaICL, specifically in tasks involving diverse input formats and label spaces.

Implications and Future Work

The results detailed in the paper suggest that the PICL framework is effective in widening the scope of PLM applications without heavily relying on task-specific fine-tuning. By utilizing intrinsic tasks from a plaintext corpus, the approach scales the model's utility while avoiding undue bias towards any specific dataset format.

This work hints at several promising research continuations:

• Expansion of Intrinsic Task Libraries: Further work could explore even more sophisticated methods for detecting intrinsic tasks which may offer even richer, more diverse pre-training opportunities.
• Zero-Shot Instruction Following: Incorporating explicit instruction prompts into the pre-training process could improve zero-shot capabilities, providing a direct pathway to developing models capable of understanding and executing novel user-defined tasks without retraining.
• Resource Optimization: Addressing the computational overhead for training large models with this methodology could also augment the accessibility and practical utility of the PICL framework.

In summary, this paper contributes a robust approach to the enhancement of in-context learning in LLMs, establishing a foundation for further exploration within the field of more generalized and efficient machine intelligence frameworks.