https://github.com/therock90421/da-ada
공식 코드DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Papers with Code 아카이브
General Knowledge
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 1
- 보존된 평가 기록
- 2
This task aims to evaluate the ability of a model to answer general-knowledge questions.
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thomasscialom/beametrics
공식 코드BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation
https://github.com/thunlp/fastfid
공식 코드FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
https://github.com/tmvllrrl/its-study
공식 코드Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
https://github.com/trustworthy-ml-lab/effective_skill_unlearning
공식 코드Effective Skill Unlearning through Intervention and Abstention
https://github.com/tsinghua-fib-lab/citybench
공식 코드CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
https://github.com/uic-liu-lab/dga
공식 코드Adapting a Language Model While Preserving its General Knowledge
https://github.com/ustc-starteam/genki
공식 코드https://github.com/utcsnlp/lfqa_discourse
공식 코드How Do We Answer Complex Questions: Discourse Structure of Long-form Answers
https://github.com/vbursztyn/compositional-fine-tuning
공식 코드Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models
https://github.com/vid-koci/kbctransferlearning
공식 코드Pre-training and Diagnosing Knowledge Base Completion Models
https://github.com/visinf/diagen
공식 코드https://github.com/vita-mllm/long-vita
공식 코드Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuray
https://github.com/voitijaner/Movie-RSs-Master-Thesis-Submission-Voit
공식 코드https://github.com/wangcunxiang/graph-as-tokens
공식 코드Exploiting Abstract Meaning Representation for Open-Domain Question Answering
https://github.com/wangcunxiang/rfid
공식 코드RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
https://github.com/wangjingyao07/adaptive-sampler
공식 코드https://github.com/wangyong1122/dtn
공식 코드Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
https://github.com/weeknan/dr-tune
공식 코드https://github.com/weitianxin/CLOVER
공식 코드https://github.com/wenhuchen/KGPT
공식 코드KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
https://github.com/wenhuchen/theoremqa
공식 코드https://github.com/wenkehuang/awesome-mllm-tuning
공식 코드Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
https://github.com/whitzard-ai/jade-db
공식 코드JADE: A Linguistics-based Safety Evaluation Platform for Large Language Models
https://github.com/whu-zqh/panda
공식 코드PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation
https://github.com/wildchlamydia/mivolo
공식 코드Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation
https://github.com/winogavil/winogavil-experiments
공식 코드WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
https://github.com/wusize/f-lmm
공식 코드https://github.com/wx-zhang/spu
공식 코드Overcoming Generic Knowledge Loss with Selective Parameter Update
https://github.com/wyripple/s1_bench
공식 코드S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models
https://github.com/xiaofang007/vip
공식 코드Aligning Medical Images with General Knowledge from Large Language Models
https://github.com/xiaoxinhe/tape_arxiv_2023
공식 코드https://github.com/xinghaowu/feddecomp
공식 코드https://github.com/xinyadu/eeqa
공식 코드https://github.com/xiyuan-zhou/elecbench-a-power-dispatch-evaluation-benchmark-for-large-language-models
공식 코드ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models
https://github.com/xpq-tech/pmet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.