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Found 9 papers, 5 papers with code
Make Your LLM Fully Utilize the Context
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge.
Compositional API Recommendation for Library-Oriented Code Generation
However, the performance remains unsatisfactory in generating library-oriented code, especially for the libraries not present in the training data of LLMs.
Skill-Based Few-Shot Selection for In-Context Learning
Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning.
How Do In-Context Examples Affect Compositional Generalization?
Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence.
Does Deep Learning Learn to Abstract? A Systematic Probing Framework
Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
This motivates us to propose input-tuning, which fine-tunes both the continuous prompts and the input representations, leading to a more effective way to adapt unfamiliar inputs to frozen PLMs.
Learning Algebraic Recombination for Compositional Generalization
In this paper, we propose LeAR, an end-to-end neural model to learn algebraic recombination for compositional generalization.
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Compositional Generalization by Learning Analytical Expressions
Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily.
