https://github.com/firatcekinel/fctr
공식 코드Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
Papers with Code 아카이브
Fact Checking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 7
- 보존된 평가 기록
- 19
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/firatcekinel/multi-task-fact-checking
공식 코드Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach
https://github.com/firojalam/assisting-fact-checking
공식 코드Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document
https://github.com/firojalam/detecting-previously-fact-checked-claims
공식 코드The Role of Context in Detecting Previously Fact-Checked Claims
https://github.com/flipz357/metric-csv
공식 코드https://github.com/franciellevargas/FactNews
공식 코드Predicting Sentence-Level Factuality of News and Bias of Media Outlets
https://github.com/fuxiaoliu/twitter-video-dataset
공식 코드COVID-VTS: Fact Extraction and Verification on Short Video Platforms
https://github.com/gair-nlp/factool
공식 코드https://github.com/gary-code/peifg
공식 코드Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor
https://github.com/gautamshahi/fakeclaim
공식 코드https://github.com/gkaradzhov/FactcheckingRANLP
공식 코드https://github.com/gregorbachmann/next-token-failures
공식 코드https://github.com/gtfintechlab/subjective-qa
공식 코드https://github.com/guilhermemr04/scaling-zero-shot-retrieval
공식 코드No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
https://github.com/haizelabs/verdict
공식 코드https://github.com/hanshanley/tracking-takes
공식 코드https://github.com/harryw1248/covid_19_misinformation_weakly_supervised_bwgs
공식 코드A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topics
https://github.com/hasaniqbal777/openfactcheck
공식 코드OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
https://github.com/hazyresearch/tabi
공식 코드TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
https://github.com/heng840/amig
공식 코드https://github.com/hiyouga/llama-factory
공식 코드FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs
https://github.com/ibm/kgi-slot-filling
공식 코드https://github.com/ictmcg/mtm
공식 코드https://github.com/idiap/news-media-reliability
공식 코드Reliability Estimation of News Media Sources: Birds of a Feather Flock Together
https://github.com/iinemo/lm-polygraph
공식 코드Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
https://github.com/iit-dm/fin-fact
공식 코드Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation
https://github.com/isspek/cross_lingual_checkworthy_detection
공식 코드https://github.com/isyufeng/FactFinders
공식 코드https://github.com/ixlab-ut/chiir-2022
공식 코드https://github.com/j6mes/2021-acl-factual-error-correction
공식 코드https://github.com/jadecurl/ffrr
공식 코드https://github.com/javyduck/knowhalu
공식 코드KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking
https://github.com/jiayingwu19/sheepdog
공식 코드Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
https://github.com/jiesutd/NCRFpp
공식 코드Design Challenges and Misconceptions in Neural Sequence Labeling
https://github.com/jifan-chen/fact-checking-via-raw-evidence
공식 코드Complex Claim Verification with Evidence Retrieved in the Wild
https://github.com/jinghanzhang1998/ratt
공식 코드RATT: A Thought Structure for Coherent and Correct LLM Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.