However, the crucial problem of how to improve the reliability of GPT-3 is still under-explored. While reliability is a broad and vaguely defined term, we decompose reliability into four main facets that correspond to the existing framework of ML safety and are well-recognized to be important: generalizability, social biases, calibration, and factuality. Our core contribution is to establish simple and effective prompts that improve GPT-3's reliability as it: 1) generalizes out-of-distribution, 2) balances demographic distribution and uses natural language instructions to reduce social biases, 3) calibrates output probabilities, and 4) updates the LLM's factual knowledge and reasoning chains. With appropriate prompts, GPT-3 is more reliable than smaller-scale supervised models on all these facets. We release all processed datasets, evaluation scripts, and model predictions. Our systematic empirical study not only sheds new insights on the reliability of prompting LLMs, but more importantly, our prompting strategies can help practitioners more reliably use LLMs like GPT-3. NLP is dominated by large language models (LLMs) -- pretrained on large, unlabeled text data -- that are then used for downstream tasks (Devlin et al., 2019a; Brown et al., 2020). Scaling the model and data size often brings gains on downstream tasks (Kaplan et al., 2020; BIG-Bench, 2022), allowing what some call emergent abilities (Wei et al., 2022a). These emergent behaviors are accomplished through prompting--a crafted, natural language text to shape predictions or offer relevant information without expensive supervised data. Among all the existing LLMs, GPT-3 (Brown et al., 2020) is particularly popular due to its flexibility and ease of use from the OpenAI API However, rising numbers on these evaluations do not ensure LLM reliability. For example, LLMs (including GPT-3) produce biased (Lucy & Bamman, 2021) generations, false statements (Lin et al., 2022b), and outdated information (Chen et al., 2021b; Kasai et al., 2022). Deploying such models in the real world could result in catastrophic harm. In the context of prompting LLMs, several previous works have explored their reliability. For example, in the release reports of GPT-3 (Brown et al., 2020), OPT (Zhang et al., 2022), Gopher (Rae et al., 2021) and PaLM (Chowdhery et al., 2022), there are dedicated experiments evaluating these LLMs' representational bias and toxicity.

Tokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., at the sub-character level. To utilize such information, we propose sub-character (SubChar for short) tokenization. Specifically, we first encode the input text by converting each Chinese character into a short sequence based on its glyph or pronunciation, and then construct the vocabulary based on the encoded text with sub-word segmentation. Experimental results show that SubChar tokenizers have two main advantages over existing tokenizers: 1) They can tokenize inputs into much shorter sequences, thus improving the computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos. At the same time, models trained with SubChar tokenizers perform competitively on downstream tasks. We release our code and models at https://github.com/thunlp/SubCharTokenization to facilitate future work.