While supervised fine-tuning (SFT) has been a straightforward approach for
tailoring the output of foundation large language model (LLM) to specific
preferences, concerns have been raised about the depth of this alignment, with
some critiques suggesting it is merely "superficial". We critically examine
this hypothesis within the scope of cross-lingual generation tasks, proposing
that the effectiveness of SFT may be constrained by its reliance on prior
tokens to guide cross-lingual generation. Based on this crucial insight, and in
response to the challenges posed by the costly and limited availability of
non-English data for SFT, we introduce a novel training-free alignment method
named PreTTY, which employs minimal task-related prior tokens to bridge the
foundation LLM and the SFT LLM, achieving comparable performance without
training. Experiments on machine translation and part-of-speech tagging across
eight languages demonstrate the efficacy of PreTTY in cross-lingual settings.
Remarkably, by initiating the decoding process with only one or two prior
tokens, foundation LLMs can achieve performance comparable to their SFT
counterparts. This method presents a cost-effective alternative to SFT and
advances the democratization of multilingual LLMs.

基于对有关预先令牌指导跨语言生成的依赖性的重要洞察，我们提出了一种名为 PreTTY 的新型无需训练的对齐方法，用于在基础 LLM 和 SFT LLM 之间建立桥梁，以实现可比较的性能。在八种语言上进行的机器翻译和词性标注实验表明，PreTTY 在跨语言环境中具有很高的效能。这种方法是 SFT 的一种经济高效的替代方案，推动了多语言 LLM 的民主化。

预训练语言模型中的非英文对齐引导

Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation  Language Model

Recent research has evidenced the significant potentials of Large Language
Models (LLMs) in handling challenging tasks within 3D scenes. However, current
models are constrained to addressing object-centric tasks, where each
question-answer pair focuses solely on an individual object. In real-world
applications, users may pose queries involving multiple objects or expect for
answers that precisely reference various objects. We introduce the use of
object identifiers to freely reference objects during a conversation. While
this solution appears straightforward, it presents two main challenges: 1) How
to establish a reliable one-to-one correspondence between each object and its
identifier? 2) How to incorporate complex spatial relationships among dozens of
objects into the embedding space of the LLM? To address these challenges, we
propose a two-stage alignment method, which involves learning an
attribute-aware token and a relation-aware token for each object. These tokens
capture the object's attributes and spatial relationships with surrounding
objects in the 3D scene. Once the alignment is established, we can fine-tune
our model on various downstream tasks using instruction tuning. Experiments
conducted on traditional datasets like ScanQA, ScanRefer, and Nr3D/Sr3D
showcase the effectiveness of our proposed method. Additionally, we create a 3D
scene captioning dataset annotated with rich object identifiers, with the
assistant of GPT-4. This dataset aims to further explore the capability of
object identifiers in effective object referencing and precise scene
understanding.

我们提出了一种使用对象标识符在对话中自由引用对象的方法，通过学习每个对象的属性感知令牌和关系感知令牌来解决建立可靠的对象 - 标识符一对一对应关系以及在 LLM 的嵌入空间中融入复杂的空间关系的挑战，并通过指令调整在各种下游任务上对模型进行微调，实验证明了我们提出方法的有效性，同时创建了一个包含丰富对象标识符的 3D 场景字幕数据集，以进一步探索对象标识符在有效对象引用和精确场景理解中的能力。

Chat-3D v2：通过对象标识符将 3D 场景与大型语言模型相连接

Chat-3D v2: Bridging 3D Scene and Large Language Models with Object  Identifiers

The use of language is subject to variation over time as well as across
social groups and knowledge domains, leading to differences even in the
monolingual scenario. Such variation in word usage is often called lexical
semantic change (LSC). The goal of LSC is to characterize and quantify language
variations with respect to word meaning, to measure how distinct two language
sources are (that is, people or language models). Because there is hardly any
data available for such a task, most solutions involve unsupervised methods to
align two embeddings and predict semantic change with respect to a distance
measure. To that end, we propose a self-supervised approach to model lexical
semantic change by generating training samples by introducing perturbations of
word vectors in the input corpora. We show that our method can be used for the
detection of semantic change with any alignment method. Furthermore, it can be
used to choose the landmark words to use in alignment and can lead to
substantial improvements over the existing techniques for alignment.
We illustrate the utility of our techniques using experimental results on
three different datasets, involving words with the same or different meanings.
Our methods not only provide significant improvements but also can lead to
novel findings for the LSC problem.

本文提出了一种自监督的方法，通过在输入语料库中引入单词向量的扰动来生成训练样本，用于模拟词汇语义变化，以及通过任何对齐方法检测语义变化并选择基准词，具有很大的改进潜力，并在三种不同数据集上进行实验结果展示。