Supervised Knowledge Makes Large Language Models Better In-context Learners

Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications. However, the critical challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. While previous in-context learning research has focused on enhancing models to adhere to users' specific instructions and quality expectations, and to avoid undesired outputs, little to no work has explored the use of task-Specific fine-tuned Language Models (SLMs) to improve LLMs' in-context learning during the inference stage. Our primary contribution is the establishment of a simple yet effective framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks. Using our proposed plug-in method, enhanced versions of Llama 2 and ChatGPT surpass their original versions regarding generalizability and factuality. We offer a comprehensive suite of resources, including 16 curated datasets, prompts, model checkpoints, and LLM outputs across 9 distinct tasks. Our empirical analysis sheds light on the advantages of incorporating discriminative models into LLMs and highlights the potential of our methodology in fostering more reliable LLMs.

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

Pre-trained language models (PLMs) improve the model generalization by leveraging massive data as the training corpus in the pre-training phase. However, currently, the out-of-distribution (OOD) generalization becomes a generally ill-posed problem, even for the large-scale PLMs in natural language understanding tasks, which prevents the deployment of NLP methods in the real world. To facilitate the research in this direction, this paper makes the first attempt to establish a unified benchmark named GLUE-X, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it. To this end, we collect 13 publicly available datasets as OOD test data, and conduct evaluations on 8 classic NLP tasks over \emph{18} popularly used models. Our findings confirm that the OOD accuracy in NLP tasks needs to be paid more attention to since the significant performance decay compared to ID accuracy has been found in all settings.