https://github.com/cuhealthybrains/mt-llm
공식 코드Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions
On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts
Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases
RAGViz: Diagnose and Visualize Retrieval-Augmented Generation
Detecting Manipulated Contents Using Knowledge-Grounded Inference
BASE-SQL: A powerful open source Text-To-SQL baseline approach
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
CASTILLO: Characterizing Response Length Distributions of Large Language Models
Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
Contextualization Distillation from Large Language Model for Knowledge Graph Completion
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.