Trending Research
LLaMA-Omni: Seamless Speech Interaction with Large Language Models
We build our model based on the latest Llama-3. 1-8B-Instruct model.
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
We present PaperQA, a RAG agent for answering questions over the scientific literature.
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
We also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for speech output.
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery
qhjqhj00/memorag
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Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases, thereby enhancing the generation quality of large language models (LLMs) through optimized context.
GeoCalib: Learning Single-image Calibration with Geometric Optimization
cvg/geocalib
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This single-image calibration can benefit various downstream applications like image editing and 3D mapping.
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
openbmb/minicpm
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For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data.
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas.
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA
THUDM/LongCite
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Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to their potential hallucinations.
iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models
Our method demonstrates superior performance compared to baseline methods across three scenarios: converting scientific papers to graphs, websites to graphs, and CVs to graphs.
