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Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks (2405.10548v3)

Published 17 May 2024 in cs.CL

Abstract: LLMs have transformed NLP with their remarkable In-context Learning (ICL) capabilities. Automated assistants based on LLMs are gaining popularity; however, adapting them to novel tasks is still challenging. While colossal models excel in zero-shot performance, their computational demands limit widespread use, and smaller LLMs struggle without context. This paper investigates whether LLMs can generalize from labeled examples of predefined tasks to novel tasks. Drawing inspiration from biological neurons and the mechanistic interpretation of the Transformer architecture, we explore the potential for information sharing across tasks. We design a cross-task prompting setup with three LLMs and show that LLMs achieve significant performance improvements despite no examples from the target task in the context. Cross-task prompting leads to a remarkable performance boost of 107% for LLaMA-2 7B, 18.6% for LLaMA-2 13B, and 3.2% for GPT 3.5 on average over zero-shot prompting, and performs comparable to standard in-context learning. The effectiveness of generating pseudo-labels for in-task examples is demonstrated, and our analyses reveal a strong correlation between the effect of cross-task examples and model activation similarities in source and target input tokens. This paper offers a first-of-its-kind exploration of LLMs' ability to solve novel tasks based on contextual signals from different task examples.

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Analysis of Cross-Task Prompting Capabilities in LLMs

The paper "LLMs can Learn In-context from Cross-task Prompts," investigates the capability of LLMs to generalize across tasks when exposed to labeled examples from different task domains. This work explores the concept of Cross-task Prompting within the domain of In-Context Learning (ICL), where LLMs are exemplified by their ability to infer tasks without explicit training updates. The authors focus on evaluating whether LLMs can leverage examples from a task library to perform significantly better on tasks for which they have no specific training data, offering an alternative approach to standard ICL practices.

Motivation and Challenges

The paper arises from two primary challenges: the high computational cost associated with colossal models in zero-shot regimes and the performance limitations of smaller models without in-context prompts. The explored solution leverages similarities with biological neural pathways, which often exhibit transfer learning across different limbs or tasks. By drawing parallels with the Transformer architecture's mechanistic interpretation, there is potential for leveraging learned pathways across tasks, providing context for the unprecedented adaptability observed in LLMs.

Methodology

The authors delineate a Cross-task Prompting setup using three LLMs: LLaMA-2 7B, LLaMA-2 13B, and GPT 3.5. Within this framework, experiments are conducted across various task pairs, with one task providing the source examples and another constituting the target task. Critical to their methodology is the selection of semantically similar examples from source datasets to create effective prompt contexts. The rigorous design involves a series of controlled configurations: semantic similarity selection, random instance selection, and label randomization.

Results

  • Performance Boosts: Across all models, Cross-task Prompting delivered observable performance improvements compared to zero-shot regimes. Average improvements were noted as 107% for LLaMA-2 7B, 18.6% for LLaMA-2 13B, and 3.2% for GPT 3.5. The capability to achieve near-equivalent performance to standard ICL models using unrelated task examples is a significant finding.
  • Dependence on Source Tasks: The results indicate differing efficacy based on source-target task pairings. Certain tasks like ARC-Easy consistently improved target task performance, indicating their better alignment and domain coverage. Conversely, some tasks like Conll2003-POS offered minimal improvements, suggesting domain specificity's role in information transfer.
  • Robustness of Prompting Techniques: When increasing the number of examples from source tasks, Cross-task Prompting did not necessarily yield better results. This contrasts with typical ICL setups where more examples usually lead to better outcomes, highlighting a key limitation of cross-domain learning.
  • Pseudo-label Generation: Incorporating Cross-task Prompting for generating pseudo-labels showcased marked improvements over zero-shot predictions, often rivaling the performance achieved by gold-standard labels. This highlights the potential of this approach in settings where labeled data is scarce.

Implications and Future Directions

This investigation into Cross-task Prompting demonstrates the potential for LLMs to become more versatile and accessible across various applications, reducing dependency on extensive task-specific data. The method exemplifies a critical step toward achieving training-free task generalization in AI, advancing the efficiency of LLMs in diverse application areas.

Looking forward, the development of more sophisticated alignment algorithms could further improve Cross-task Prompting effectiveness. Discovering shared neural pathways within Transformer models may unlock broader and more efficient intra-model communication, creating opportunities for improved generalizable AI systems. This research supports ongoing endeavors in demonstrating the vast potential of integrating semantic and contextual elements across seemingly disparate tasks. Recommendations for future work should focus on enhancing LLM interpretability and identifying potential limitations inherent to task dissimilarities absent from current datasets.

Conclusion

The paper effectively addresses a key limitation within the LLM landscape, proposing Cross-task Prompting as a viable route for improving LLM adaptability to novel tasks. While offering significant advancements in efficiency and applicability, this research also lays foundational insights for enhancing task generalization strategies within AI, poised to impact future model development and deployment.

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Authors (4)
  1. Anwoy Chatterjee (3 papers)
  2. Eshaan Tanwar (4 papers)
  3. Subhabrata Dutta (24 papers)
  4. Tanmoy Chakraborty (224 papers)
Citations (4)
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