Deciphering language from brain activity is a crucial task in brain-computer
interface (BCI) research. Non-invasive cerebral signaling techniques including
electroencephalography (EEG) and magnetoencephalography (MEG) are becoming
increasingly popular due to their safety and practicality, avoiding invasive
electrode implantation. However, current works under-investigated three points:
1) a predominant focus on EEG with limited exploration of MEG, which provides
superior signal quality; 2) poor performance on unseen text, indicating the
need for models that can better generalize to diverse linguistic contexts; 3)
insufficient integration of information from other modalities, which could
potentially constrain our capacity to comprehensively understand the intricate
dynamics of brain activity.
This study presents a novel approach for translating MEG signals into text
using a speech-decoding framework with multiple alignments. Our method is the
first to introduce an end-to-end multi-alignment framework for totally unseen
text generation directly from MEG signals. We achieve an impressive BLEU-1
score on the $\textit{GWilliams}$ dataset, significantly outperforming the
baseline from 5.49 to 10.44 on the BLEU-1 metric. This improvement demonstrates
the advancement of our model towards real-world applications and underscores
its potential in advancing BCI research. Code is available at
$\href{https://github.com/NeuSpeech/MAD-MEG2text}{this https URL}$.

本研究提出了一种使用多重对齐框架将脑电信号转换为文本的新方法，通过在 MEG 信号中直接生成全新的文本，实现了令人印象深刻的 BLEU-1 得分，极大地超越了基线模型，显示出实现 BCI 研究的潜力。

MAD：多对多 MEG 到文本解码

MAD: Multi-Alignment MEG-to-Text Decoding

We present a comprehensive evaluation of large language models(LLMs)' ability
to reason about composition relations through a benchmark encompassing 1,500
test cases in English, designed to cover six distinct types of composition
relations: Positional, Comparative, Personal, Mathematical, Identity, and
Other. Acknowledging the significance of multilingual capabilities, we expanded
our assessment to include translations of these cases into Chinese, Japanese,
French, and Korean. Our Multilingual Composition Relation (MCR) benchmark aims
at investigating the robustness and adaptability of LLMs in handling
composition relation reasoning across diverse linguistic contexts.

通过包含 1,500 个测试案例的评估，我们在英语中全面评估了大型语言模型（LLMs）在推理组合关系方面的能力，旨在了解 LLMs 处理不同语言环境下的组合关系推理的鲁棒性和适应性。

大型语言模型在组合性关系推理中的限制探索

Exploring the Limitations of Large Language Models in Compositional  Relation Reasoning

Most current approaches to metaphor identification use restricted linguistic
contexts, e.g. by considering only a verb's arguments or the sentence
containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that
broader discourse features are crucial for better metaphor identification. We
train simple gradient boosting classifiers on representations of an utterance
and its surrounding discourse learned with a variety of document embedding
methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam
metaphor identification task without the complex metaphor-specific features or
deep neural architectures employed by other systems. A qualitative analysis
further confirms the need for broader context in metaphor processing.

本文提出，语境特征对于更好的隐喻识别非常重要，我们基于多种文档嵌入方法对话语及其周围环境进行表征，使用简单的梯度增强分类器，在不使用其他系统采用的复杂隐喻特定特征或深度网络体系结构的情况下，达到了几乎最先进的 2018 年 VU 阿姆斯特丹隐喻识别任务数据集的结果，并进一步通过质性分析证实了在隐喻处理中需要更广泛的上下文环境。