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Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitrary task can be reformulated as a natural language prompt, and a language model can be asked to generate the completion, indirectly performing the task in a paradigm known as prompt-based learning. To date, emergent prompt-based learning capabilities have mainly been demonstrated for unidirectional language models. However, bidirectional language models pre-trained on denoising objectives such as masked language modeling produce stronger learned representations for transfer learning. This motivates the possibility of prompting bidirectional models, but their pre-training objectives have made them largely incompatible with the existing prompting paradigm. We present SAP (Sequential Autoregressive Prompting), a technique that enables the prompting of bidirectional models. Utilizing the machine translation task as a case study, we prompt the bidirectional mT5 model (Xue et al., 2021) with SAP and demonstrate its few-shot and zero-shot translations outperform the few-shot translations of unidirectional models like GPT-3 and XGLM (Lin et al., 2021), despite mT5's approximately 50% fewer parameters. We further show SAP is effective on question answering and summarization. For the first time, our results demonstrate prompt-based learning is an emergent property of a broader class of language models, rather than only unidirectional models.
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This content will become publicly available on August 16, 2025
Language-guided World Models: A Model-based Approach to AI Control
This paper introduces the concept of Language- Guided World Models (LWMs)—probabilistic models that can simulate environments by read- ing texts. Agents equipped with these models provide humans with more extensive and effi- cient control, allowing them to simultaneously alter agent behaviors in multiple tasks via nat- ural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our exper- iments reveal the lack of generalizability of the state-of-the-art Transformer model, as it of- fers marginal improvements in simulation qual- ity over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the perfor- mance of a model with an oracle semantic pars- ing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to re- vise plans based on their language feedback.
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- Award ID(s):
- 2107048
- PAR ID:
- 10542083
- Publisher / Repository:
- ACL 2024 Workshop SpLU-RoboNLP
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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