cs.CL（2024-04-01）

📊 共 15 篇论文 | 🔗 3 篇有代码

🎯 兴趣领域导航

支柱九：具身大模型 (Embodied Foundation Models) (13 🔗2) 支柱三：空间感知与语义 (Perception & Semantics) (1) 支柱二：RL算法与架构 (RL & Architecture) (1 🔗1)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (13 篇)

#题目一句话要点标签🔗⭐
1 How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey 探讨大型语言模型在社会辅助机器人交互中的应用与挑战 large language model multimodal
2 A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules 提出统计框架以优化大型语言模型水印检测 large language model
3 Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs 提出GraphEval以解决大型语言模型的事实性评估问题 large language model ✅
4 Token-Efficient Leverage Learning in Large Language Models 提出Token-Efficient Leverage Learning以解决低资源场景下LLM性能不足问题 large language model
5 Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models 提出可控大型语言模型以平衡安全性与帮助性问题 large language model
6 Evalverse: Unified and Accessible Library for Large Language Model Evaluation 提出Evalverse以统一评估大型语言模型的工具 large language model
7 A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias 综述多语言大语言模型以解决语言不平衡与偏见问题 large language model
8 Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs 提出位置感知参数高效微调方法以减少LLMs中的位置偏差 large language model
9 Mapping the Increasing Use of LLMs in Scientific Papers 通过系统分析揭示LLM在科学论文中的使用趋势 large language model
10 Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit 研究LLMs如何模拟人类对事实驱动问题的回答 large language model
11 LLM Attributor: Interactive Visual Attribution for LLM Generation 提出LLM Attributor以解决LLM文本生成的可追溯性问题 large language model
12 FABLES: Evaluating faithfulness and content selection in book-length summarization 提出FABLES评估框架以解决长文档摘要的忠实性问题 large language model
13 Source-Aware Training Enables Knowledge Attribution in Language Models 提出源感知训练以解决语言模型知识归属问题 large language model ✅

🔬 支柱三：空间感知与语义 (Perception & Semantics) (1 篇)

#题目一句话要点标签🔗⭐
14 Open-Vocabulary Federated Learning with Multimodal Prototyping 提出联邦多模态原型方法以解决开放词汇学习问题 open-vocabulary open vocabulary multimodal

🔬 支柱二：RL算法与架构 (RL & Architecture) (1 篇)

#题目一句话要点标签🔗⭐
15 Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment 提出MBR-BoN以解决奖励模型不准确导致的奖励黑客问题 preference learning RLHF DPO ✅

⬅️ 返回 cs.CL 首页 · 🏠 返回主页