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.
658b97a9135580745c460614
Linyi Yang
claimed_verified
2023-12-29T08:59:38.465Z
64895683f534abe18eec264b
/avatars/73cc9e6db6db86793787750776b57c63.svg
Linyi Yang
user
linyiyang2023
658b97a9135580745c460615
Shuibai Zhang
658b97a9135580745c460616
Zhuohao Yu
claimed_verified
2024-06-24T07:34:51.040Z
61e24808b31e7cc38eb84d37
/avatars/65fbea940fad211462ecc5ad725e0c28.svg
Zhuohao Yu
user
zhuohaoyu
658b97a9135580745c460617
Guangsheng Bao
658b97a9135580745c460618
Yidong Wang
658b97a9135580745c460619
Jindong Wang
claimed_verified
2025-06-02T08:02:42.285Z
6204cc0d522e40b4a18d86e2
/avatars/18daf2de5671e711dc745388dd60569d.svg
Jindong Wang
user
jindongwang
658b97a9135580745c46061a
Ruochen Xu
658b97a9135580745c46061b
Wei Ye
658b97a9135580745c46061c
Xing Xie
658b97a9135580745c46061d
Weizhu Chen
658b97a9135580745c46061e
Yue Zhang
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.
A framework using task-specific fine-tuning improves large language models' generalizability and factuality during inference by leveraging discriminative models.
Large Language Models in-context learning prompt engineering generative models task-Specific fine-tuned Language Models discriminative models out-of-distribution data hallucinations generalizability factuality