The emerging field of language dynamics
S. Wichmann
一位语言学家的简单评论，引用了许多物理学家的文章：定量方法，基于代理的计算机模拟，语言动力学，语言类型学，历史语言学
A simple review by a linguist, citing many articles by physicists: Quantitative methods, agent-based computer simulations, language dynamics, language typology, historical linguistics
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Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness and/or non-linguists' perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusabili…
The ever-growing size of the foundation language model has brought significant performance gains in various types of downstream tasks. With the existence of side-effects brought about by the large size of the foundation language model such as deployment cost, availability issues, and environmental cost, there is some interest in exploring other possible directions, such as a divide-and-conquer scheme. In this paper, we are asking a basic question: are language processes naturally dividable? We study this problem with a simple two-tower language model setting, where two language models with ide…
We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better …
Translations capture important information about languages that can be used as implicit supervision in learning linguistic properties and semantic representations. In an information-centric view, translated texts may be considered as semantic mirrors of the original text and the significant variations that we can observe across various languages can be used to disambiguate a given expression using the linguistic signal that is grounded in translation. Parallel corpora consisting of massive amounts of human translations with a large linguistic variation can be applied to increase abstractions a…
Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world, a PTLM-based model must deal with data distributions that deviate from what the PTLM was initially trained on. In this paper, we study a lifelong language model pretraining challenge where a PTLM is continually updated so as to adapt to emerging data. Over a domain-incremental research paper stream and a chronologically-ordered tweet stream, we incrementally pretrain a PTLM with different continual learning algorithm…
ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models
针对现有语音语言模型主流训练范式在韵律学习上的不足，提出ProsodyLM，通过引入便于学习韵律的词级韵律tokenization方案，使模型仅通过预训练即可获得多样化的新兴韵律处理能力。
Bringing Emerging Architectures to Sequence Labeling in NLP
研究新兴架构在不同序列标注任务中的适应性，发现其在简单任务中的表现不适用于复杂任务和多语言场景。
Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions
本文系统性地调查了大型语言模型（LLM）的发展、影响与局限性，全面概述了其研究目标、方法、伦理挑战及社会影响，并指出了未来发展的潜在方向。
Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhanced research interests in the intersection of the Vision and Language arena with its numerous applications and fast-paced growth. In this paper, we present a detailed overview of the latest trends in research pertaining to visual and language modalities. We look at its applications in their task formulations and how to solve various problems related to semantic perception and content generation. We also address task-…
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
本文系统综述多模态大语言模型的推理能力，梳理现有评估协议、前沿模型与推理密集型应用，并指出当前实践问题及未来方向。
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
该研究提出LLM驱动的在线健康社区新兴观点挖掘框架，将其建模为Reddit帖文标题-评论对的成对立场检测问题，发布长新冠主题的LC-stance数据集，验证GPT-4零样本立场检测性能更优，并分析模型误差来源。
The majority of language domains require prudent use of terminology to ensure clarity and adequacy of information conveyed. While the correct use of terminology for some languages and domains can be achieved by adapting general-purpose MT systems on large volumes of in-domain parallel data, such quantities of domain-specific data are seldom available for less-resourced languages and niche domains. Furthermore, as exemplified by COVID-19 recently, no domain-specific parallel data is readily available for emerging domains. However, the gravity of this recent calamity created a high demand for re…
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
本文系统综述了大语言模型面临的版权、偏见、隐私等长期伦理问题及真实性、社会规范等新困境，并强调应将伦理标准与社会价值观融入模型开发，以推动负责任的语言模型发展。
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
本文探索ChatGPT仅通过提示进行情感计算的零样本能力，发现其在效价、唤醒度、支配度维度上有意义的情感分析，并能基于OCC模型进行情境评价，表明复杂情感处理能力从语言模型中涌现，对情感分析、社交机器人等应用有重要意义。
DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
针对RAG中新兴语言表达阻碍检索并导致幻觉的问题，提出DynamicER基准及具有持续适应能力的时间分段聚类方法，有效解析动态实体提及并提升RAG模型的QA性能。
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers
该研究通过30名专业作家参与的实证实验，探究大语言模型在创意写作中的辅助作用，发现其在转译和审阅环节更具效用，为相关研究指明了方向。
Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions
本文全面综述了GPT的架构、工作原理、训练流程及使能技术，探讨了其在各领域的应用影响，并分析了当前面临的挑战与未来发展方向。
Validating LLMs in social science: Epistemic threats and emerging norms
研究探讨大语言模型对社会科学方法论的重塑，通过分析八本旗舰期刊论文语料库，发现其生成测量在实证分析中核心作用凸显，但验证实践存问题，进而概述补充策略以完善大语言模型在社会科学应用的规范标准。
We investigate the generation of new concepts from combinations of properties as an artificial language develops. To do so, we have developed a new framework for conjunctive concept combination. This framework gives a semantic grounding to the weighted sum approach to concept combination seen in the literature. We implement the framework in a multi-agent simulation of language evolution and show that shared combination weights emerge. The expected value and the variance of these weights across agents may be predicted from the distribution of elements in the conceptual space, as determined by t…
Uncovering Customer Issues through Topological Natural Language Analysis
针对电商客服中具体问题难以快速识别的难题，提出融合自然语言技术与拓扑数据分析的端到端深度学习框架，通过句子标注、嵌入白化与无向图拓扑特征检测新兴和趋势性客户问题，且结果与新闻来源高度一致。
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages. In this survey paper, we attempt to provide an overview of the growing body of research in this space. We begin by reviewing natural language semantic parsing techniques and draw parallels with program synthesis efforts. We then consider semantic parsing works from an evolutionary perspective, with specific analyses on neuro-symbolic methods, architecture, and supervision. We then…
The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends
本综述通过五维视角分析大型语言模型中注意力机制的演变，揭示显式记忆与循环方法融合、跨层协调及多维记忆路由假设，强调高效架构设计正转向上下文记忆的组织与选择性使用。
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media
针对极端主义社交媒体中快速演变的编码式反犹语言，本文提出结合频率、语法过滤、微调 LLM 语义相似性和犹太主题词排除的新术语发现流程。
Mapping the Emerging Social Science of Large Language Models
该研究通过大规模语料库和多种文本分析方法，构建了LLM社会科学的三领域分类体系，揭示研究分布与场所差异，为理解其社会影响提供可复现框架。
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
针对联邦指令微调中LLM安全对齐易受隐蔽攻击的问题，提出自动化攻击数据生成攻击及事后自动化防御流程，实验验证攻击显著降低安全率且防御可大幅恢复。
The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints
本文探讨了低资源NLP评估中由于标注资源不足导致的矛盾，分析了过去十年中评估方法的发展阶段，并提出了新的解决方案以提升评估的公平性和有效性。
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers
本文首次从多语言对齐视角系统综述多语言大语言模型，提出新分类体系，梳理前沿挑战，并汇总论文、语料与排行榜等开源资源。
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
本文提出药物分子与适应症相互翻译的新任务，测试九种T5大模型变体在ChEMBL和DrugBank数据集上的表现，展示LLM在加速药物发现中的潜力与局限。
Resource Consumption Threats in Large Language Models
本文系统回顾了大语言模型中资源消耗威胁，分析其影响并提出统一视角，旨在明确问题领域以支持后续建模和缓解措施。
Large Language Models as Psychological Simulators: A Methodological Guide
本文探讨利用大语言模型进行心理模拟的两种主要应用：角色与人设模拟及认知过程建模，并提出方法框架与验证策略，强调透明性与伦理考量。
Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly", "John hikes", "Fire makes smoke"). Though found in every language and emerging early in development, the language of generalization is philosophically puzzling and has resisted precise formalization. Here, we propose the first formal account of generalizations conveyed with language that makes quantitative predictions about human understanding. We test our model in three diverse domains: generalizations about categories (generic language), events (habitual language), and causes (causal lang…
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
本文综述了大型语言模型的知识蒸馏与数据集蒸馏技术，探讨了其在压缩模型、保持推理能力及语言多样性方面的挑战与未来发展方向。
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization
本综述系统梳理了大型语言模型在文本引导的分子生成与优化任务中的最新进展，提出分类体系并总结常用数据集与评估方法，展望未来挑战。
Large Language Model as a Universal Clinical Multi-task Decoder
针对临床多任务管理与新任务适配难题，提出以预训练大语言模型为通用临床多任务解码器的范式，新增任务仅需指令模板，在数百任务上表现媲美传统方法，新任务适配性与数据效率优异。
MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation
本文提出多任务语音语言模型MSLM，一种仅解码器架构，在不依赖文本数据的情况下实现保留说话人风格的多语言语音到语音翻译。
Can ChatGPT's Responses Boost Traditional Natural Language Processing?
本文研究 ChatGPT 的详细回答与现有 NLP 方法融合能否提升情感计算性能，在情感分析、自杀倾向检测和大五人格评估三个任务上验证了其新颖知识可通过早期或晚期融合改进传统模型。
A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models
本文综述了量化图表示学习，探讨了其与大语言模型的整合方法及未来研究方向。
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