cs.CL（2024-06-29）

📊 共 14 篇论文 | 🔗 1 篇有代码

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支柱九：具身大模型 (Embodied Foundation Models) (12) 支柱二：RL算法与架构 (RL & Architecture) (2 🔗1)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (12 篇)

#题目一句话要点标签🔗⭐
1 How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models 利用多模态开放模型进行知识迁移，提升事实核查器性能 large language model foundation model multimodal
2 Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability 提出Self-Translate-Train方法，提升大型语言模型在跨语言迁移中的性能。 large language model zero-shot transfer
3 Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis 提出迭代数据生成框架IDG，利用大语言模型提升面向方面的情感分析性能。 large language model instruction following
4 ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees 提出基于保形预测的语言模型不确定性量化方法 large language model
5 Answering real-world clinical questions using large language model based systems 利用大型语言模型系统回答真实临床问题，RAG和Agentic LLM表现突出 large language model
6 PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models 提出PFME框架，用于大语言模型中细粒度幻觉的检测与编辑 large language model
7 A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model 研究参考知识选择对LLM生成SAPPhIRE模型相关技术内容的影响 large language model
8 Large Language Models for Power Scheduling: A User-Centric Approach 提出基于LLM的电力调度架构，实现用户语音请求到资源分配的转换 large language model
9 A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models 研究平行语料在多语言大模型中的有效利用策略 large language model
10 Financial Knowledge Large Language Model 提出IDEA-FinBench、IDEA-FinKER和IDEA-FinQA，提升LLM在金融领域的知识和应用能力 large language model
11 It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization 提出MM-MO，通过多目标优化实现大语言模型高效融合，无需人工干预。 large language model
12 LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods 提出ZGPTDA，利用缩放律指导GPT-4数据增强，提升少样本文本分类性能 large language model

🔬 支柱二：RL算法与架构 (RL & Architecture) (2 篇)

#题目一句话要点标签🔗⭐
13 Advancing Process Verification for Large Language Models via Tree-Based Preference Learning 提出基于树搜索偏好学习的验证器Tree-PLV，提升LLM推理过程验证精度。 preference learning large language model
14 LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement 提出LLMs-as-Instructors框架，利用大模型自动提升小模型性能 contrastive learning large language model ✅

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