Summary of Mccoder: Streamlining Motion Control with Llm-assisted Code Generation and Rigorous Verification, by Yin Li et al.
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verificationby Yin Li, Liangwei Wang,…
Summary of Class-rag: Real-time Content Moderation with Retrieval Augmented Generation, by Jianfa Chen et al.
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generationby Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen…
Summary of To Trust or Not to Trust? Enhancing Large Language Models’ Situated Faithfulness to External Contexts, by Yukun Huang et al.
To Trust or Not to Trust? Enhancing Large Language Models’ Situated Faithfulness to External Contextsby…
Summary of Scopeqa: a Framework For Generating Out-of-scope Questions For Rag, by Zhiyuan Peng and Jinming Nian and Alexandre Evfimievski and Yi Fang
ScopeQA: A Framework for Generating Out-of-Scope Questions for RAGby Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski,…
Summary of Integrating Temporal Representations For Dynamic Memory Retrieval and Management in Large Language Models, by Yuki Hou and Haruki Tamoto and Homei Miyashita
Integrating Temporal Representations for Dynamic Memory Retrieval and Management in Large Language Modelsby Yuki Hou,…
Summary of Mirage-bench: Automatic Multilingual Benchmark Arena For Retrieval-augmented Generation Systems, by Nandan Thakur et al.
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systemsby Nandan Thakur, Suleman Kazi, Ge Luo,…
Summary of Faithbench: a Diverse Hallucination Benchmark For Summarization by Modern Llms, By Forrest Sheng Bao et al.
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsby Forrest Sheng Bao, Miaoran Li,…
Summary of Enhancing Long Context Performance in Llms Through Inner Loop Query Mechanism, by Yimin Tang et al.
Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanismby Yimin Tang, Yurong Xu,…
Summary of A Comprehensive Survey Of Retrieval-augmented Generation (rag): Evolution, Current Landscape and Future Directions, by Shailja Gupta et al.
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directionsby Shailja Gupta,…
Summary of Dynamicer: Resolving Emerging Mentions to Dynamic Entities For Rag, by Jinyoung Kim et al.
DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAGby Jinyoung Kim, Dayoon Ko, Gunhee KimFirst…