https://github.com/sen33/end-to-end-dialogue-system
공식 코드A Comparative Study on Language Models for Task-Oriented Dialogue Systems
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A Comparative Study on Language Models for Task-Oriented Dialogue Systems
Controllable Neural Dialogue Summarization with Personal Named Entity Planning
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
HalluShift: Measuring Distribution Shifts towards Hallucination Detection in LLMs
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination
Investigating Memorization of Conspiracy Theories in Text Generation
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
Reducing Hallucinations in Vision-Language Models via Latent Space Steering
R-Tuning: Instructing Large Language Models to Say `I Don't Know'
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation
Towards General Visual-Linguistic Face Forgery Detection(V2)
3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Automated Hypothesis Validation with Agentic Sequential Falsifications
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
Circuit Transformer: A Transformer That Preserves Logical Equivalence
Zero-Resource Hallucination Prevention for Large Language Models
Diving Deep into Modes of Fact Hallucinations in Dialogue Systems
GraphArena: Benchmarking Large Language Models on Graph Computational Problems
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
Mitigating Multilingual Hallucination in Large Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.