cs.CL（2024-04-26）

📊 共 15 篇论文 | 🔗 1 篇有代码

🎯 兴趣领域导航

支柱九：具身大模型 (Embodied Foundation Models) (11 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (3) 支柱一：机器人控制 (Robot Control) (1)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (11 篇)

#题目一句话要点标签🔗⭐
1 HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models 提出HateTinyLLM以解决仇恨言论检测问题 large language model
2 Empowering Large Language Models for Textual Data Augmentation 提出自动生成文本数据增强指令的方法以提升LLM性能 large language model
3 The Mercurial Top-Level Ontology of Large Language Models 提出一种方法以系统化分析大型语言模型的隐含本体承诺 large language model
4 A Comprehensive Evaluation on Event Reasoning of Large Language Models 提出EV2基准以评估大型语言模型的事件推理能力 large language model
5 Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System 提出Ruffle&Riley以解决对话式辅导系统内容创作成本高的问题 large language model
6 Aging Up AAC: An Introspection on Augmentative and Alternative Communication Applications for Autistic Adults 提出用户视角的AAC工具设计以改善自闭症成人沟通问题 large language model
7 CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving 提出CoMM框架以提升LLMs在复杂科学问题上的推理能力 large language model ✅
8 PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games 提出PLAYER*以解决谋杀谜题游戏中的多智能体沟通问题 large language model
9 Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM 提出细粒度反馈强化检索以优化新闻事实核查 large language model
10 Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes 通过双重认知过程提出提示技术以减少LLMs中的社会偏见 chain-of-thought
11 Small Language Models Need Strong Verifiers to Self-Correct Reasoning 提出小型语言模型自我修正的新方法以提升推理能力 large language model

🔬 支柱二：RL算法与架构 (RL & Architecture) (3 篇)

#题目一句话要点标签🔗⭐
12 2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion 提出2M-NER以解决多语言多模态命名实体识别问题 contrastive learning multimodal
13 When to Trust LLMs: Aligning Confidence with Response Quality 提出CONQORD以解决LLMs信任度与响应质量不一致问题 reinforcement learning large language model
14 A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition 提出统一标签感知对比学习框架以解决少样本命名实体识别问题 contrastive learning

🔬 支柱一：机器人控制 (Robot Control) (1 篇)

#题目一句话要点标签🔗⭐
15 Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs 提出新方法探究大型语言模型对无意义输入的理解能力 manipulation large language model

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