Open-sourced large language models (LLMs) have demonstrated remarkable
efficacy in various tasks with instruction tuning. However, these models can
sometimes struggle with tasks that require more specialized knowledge such as
translation. One possible reason for such deficiency is that instruction tuning
aims to generate fluent and coherent text that continues from a given
instruction without being constrained by any task-specific requirements.
Moreover, it can be more challenging for tuning smaller LLMs with lower-quality
training data. To address this issue, we propose a novel framework using
examples in comparison to teach LLMs to learn translation. Our approach
involves presenting the model with examples of correct and incorrect
translations and using a preference loss to guide the model's learning. We
evaluate our method on WMT2022 test sets and show that it outperforms existing
methods. Our findings offer a new perspective on fine-tuning LLMs for
translation tasks and provide a promising solution for generating high-quality
translations. Please refer to Github for more details:
this https URL

通过比较示例教授大型语言模型（LLMs）学习翻译，提出了一种新颖的框架，使用偏好损失引导模型学习，实验结果表明该方法胜过现有方法，为生成高质量的翻译提供了一个有希望的解决方案。

TIM: 使用比较方法教授大型语言模型翻译

TIM: Teaching Large Language Models to Translate with Comparison

Recent work has shown exciting promise in updating large language models with
new memories, so as to replace obsolete information or add specialized
knowledge. However, this line of work is predominantly limited to updating
single associations. We develop MEMIT, a method for directly updating a
language model with many memories, demonstrating experimentally that it can
scale up to thousands of associations for GPT-J (6B) and GPT-NeoX (20B),
exceeding prior work by orders of magnitude. Our code and data are at
this https URL.

我们开发了 MEMIT 方法，直接更新带有许多记忆的语言模型，可以扩展到 GPT-J（6B）和 GPT-NeoX（20B）的成千上万的关联，超过以前的工作数个数量级。