Transformer-based Large Language Models (LLMs) have shown exceptional
language generation capabilities in response to text-based prompts. However,
controlling the direction of generation via textual prompts has been
challenging, especially with smaller models. In this work, we explore the use
of Prompt Tuning to achieve controlled language generation. Generated text is
steered using prompt embeddings, which are trained using a small language
model, used as a discriminator. Moreover, we demonstrate that these prompt
embeddings can be trained with a very small dataset, with as low as a few
hundred training examples. Our method thus offers a data and parameter
efficient solution towards controlling language model outputs. We carry out
extensive evaluation on four datasets: SST-5 and Yelp (sentiment analysis),
GYAFC (formality) and JIGSAW (toxic language). Finally, we demonstrate the
efficacy of our method towards mitigating harmful, toxic, and biased text
generated by language models.

利用小型语言模型进行 Prompt 调整，使用提示嵌入对生成文本进行控制，验证其在情感分析、正式度和有害语言领域的有效性。

插入并玩：一种控制文本生成的提示调整方法

Plug and Play with Prompts: A Prompt Tuning Approach for Controlling  Text Generation

Recent advancements in diffusion models have showcased their impressive
capacity to generate visually striking images. Nevertheless, ensuring a close
match between the generated image and the given prompt remains a persistent
challenge. In this work, we identify that a crucial factor leading to the
text-image mismatch issue is the inadequate cross-modality relation learning
between the prompt and the output image. To better align the prompt and image
content, we advance the cross-attention with an adaptive mask, which is
conditioned on the attention maps and the prompt embeddings, to dynamically
adjust the contribution of each text token to the image features. This
mechanism explicitly diminishes the ambiguity in semantic information embedding
from the text encoder, leading to a boost of text-to-image consistency in the
synthesized images. Our method, termed MaskDiffusion, is training-free and
hot-pluggable for popular pre-trained diffusion models. When applied to the
latent diffusion models, our MaskDiffusion can significantly improve the
text-to-image consistency with negligible computation overhead compared to the
original diffusion models.

优化了扩散模型中文本和图像的一致性，通过引入自适应掩码调整文本令牌对图像特征的贡献，提高了合成图像的质量。

MaskDiffusion：使用条件掩蔽提升文本与图像的一致性

MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask

Integrating automatic speech scoring/assessment systems has become a critical
aspect of second-language speaking education. With self-supervised learning
advancements, end-to-end speech scoring approaches have exhibited promising
results. However, this study highlights the significant decrease in the
performance of speech scoring systems in new question contexts, thereby
identifying this as a cold start problem in terms of items. With the finding of
cold-start phenomena, this paper seeks to alleviate the problem by following
methods: 1) prompt embeddings, 2) question context embeddings using BERT or
CLIP models, and 3) choice of the pretrained acoustic model. Experiments are
conducted on TOEIC speaking test datasets collected from
English-as-a-second-language (ESL) learners rated by professional TOEIC
speaking evaluators. The results demonstrate that the proposed framework not
only exhibits robustness in a cold-start environment but also outperforms the
baselines for known content.

本研究提出了一种在冷启动环境下具有鲁棒性的自动语音评分框架，使用自我监督学习方法，通过使用提示嵌入、问题上下文嵌入和预训练语音模型等方法解决了冷启动问题，并在评价已知内容方面优于基线。