# Bidirectional Language Models Are Also Few-shot Learners

arXiv: https://arxiv.org/abs/2209.14500 · HTML: https://mlsanity.com/paper/2209.14500/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 56 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)

## Built on by (most cited first, 30 of 48)

- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Prompting PaLM for Translation: Assessing Strategies and Performance](https://mlsanity.com/paper/2211.09102/) (2022)
- [Faithful Logical Reasoning via Symbolic Chain-of-Thought](https://mlsanity.com/paper/2405.18357/) (2024)
- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [Nonparametric Masked Language Modeling](https://mlsanity.com/paper/2212.01349/) (2022)
- [Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning](https://mlsanity.com/paper/2308.12219/) (2023)
- [RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models](https://mlsanity.com/paper/2308.07922/) (2023)
- [HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems](https://mlsanity.com/paper/2411.02959/) (2024)
- [This Land is {Your, My} Land: Evaluating Geopolitical Biases in Language Models](https://mlsanity.com/paper/2305.14610/) (2023)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [DocTER: Evaluating Document-based Knowledge Editing](https://mlsanity.com/paper/2308.09954/) (2023)
- [Cognitive Foundations for Reasoning and Their Manifestation in LLMs](https://mlsanity.com/paper/2511.16660/) (2025)
- [Meet in the Middle: A New Pre-training Paradigm](https://mlsanity.com/paper/2303.07295/) (2023)
- [From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation](https://mlsanity.com/paper/2502.00330/) (2025)
- [StableMask: Refining Causal Masking in Decoder-only Transformer](https://mlsanity.com/paper/2402.04779/) (2024)
- [Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions](https://mlsanity.com/paper/2304.03816/) (2023)
- [Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias](https://mlsanity.com/paper/2402.00322/) (2024)
- [Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability](https://mlsanity.com/paper/2510.00565/) (2025)
- [Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?](https://mlsanity.com/paper/2212.08986/) (2022)
- [PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction](https://mlsanity.com/paper/2510.05613/) (2025)
- [Scaling In-Context Demonstrations with Structured Attention](https://mlsanity.com/paper/2307.02690/) (2023)
- [MC-NEST: Enhancing Mathematical Reasoning in Large Language Models leveraging a Monte Carlo Self-Refine Tree](https://mlsanity.com/paper/2411.15645/) (2024)
- [Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks](https://mlsanity.com/paper/2505.11665/) (2025)
- [Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG](https://mlsanity.com/paper/2601.07192/) (2026)
- [SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging](https://mlsanity.com/paper/2403.16578/) (2024)
- [RAGent: Retrieval-based Access Control Policy Generation](https://mlsanity.com/paper/2409.07489/) (2024)
- [ENTP: Encoder-only Next Token Prediction](https://mlsanity.com/paper/2410.01600/) (2024)
- [Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter](https://mlsanity.com/paper/2408.10573/) (2024)
- [Leveraging Domain Knowledge at Inference Time for LLM Translation: Retrieval versus Generation](https://mlsanity.com/paper/2503.05010/) (2025)

## Abstract (arXiv)

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.