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Instruction Following
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Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/anonrabit/dialfred
공식 코드DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
https://github.com/anujinho/code
공식 코드https://github.com/apple/ml-gie-bench
공식 코드GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
https://github.com/apple/ml-internal-llms-instruction-following
공식 코드https://github.com/apple/ml-mia-bench
공식 코드MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs
https://github.com/apple/ml-uncertainty-llms-instruction-following
공식 코드https://github.com/architsharma97/dpo-rlaif
공식 코드A Critical Evaluation of AI Feedback for Aligning Large Language Models
https://github.com/artidoro/qlora
공식 코드https://github.com/aryopg/decore
공식 코드DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
https://github.com/audiollms/audiobench
공식 코드AudioBench: A Universal Benchmark for Audio Large Language Models
https://github.com/azshue/autopoison
공식 코드https://github.com/bahuia/dog-instruct
공식 코드DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping
https://github.com/baichenjia/copo
공식 코드Online Preference Alignment for Language Models via Count-based Exploration
https://github.com/bashivanlab/iwisdm
공식 코드IWISDM: Assessing instruction following in multimodal models at scale
https://github.com/bashlab/llasa
공식 코드LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors
https://github.com/batra-mlp-lab/vln-chasing-ghosts
공식 코드Chasing Ghosts: Instruction Following as Bayesian State Tracking
https://github.com/bcdnlp/faithscore
공식 코드FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
https://github.com/beyonderxx/trace
공식 코드TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
https://github.com/bflashcp3f/schema-to-json
공식 코드Schema-Driven Information Extraction from Heterogeneous Tables
https://github.com/bfshi/toast
공식 코드https://github.com/bhyang/diffusion-es
공식 코드https://github.com/bibisbar/LLaVA-Steering
공식 코드https://github.com/bio-nlp/medqa-cs
공식 코드MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI-SCE Framework
https://github.com/blazejosinski/lm_nav
공식 코드LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
https://github.com/bolin97/awesome-instruction-selector
공식 코드https://github.com/briemadu/codraw-icr-v1
공식 코드https://github.com/briemadu/icr-actions
공식 코드https://github.com/brightjade/Opt-Out
공식 코드Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport
https://github.com/brightjade/snap-unlearning
공식 코드Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport
https://github.com/camel-ai/camel
공식 코드CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
https://github.com/caotians1/BabyAIPlusPlus
공식 코드Zero-Shot Compositional Policy Learning via Language Grounding
https://github.com/casia-lm/mods
공식 코드https://github.com/chasonshi/melora
공식 코드MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
https://github.com/chengez/adversarial-paraphrasing
공식 코드Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text
https://github.com/chenhan97/timellama
공식 코드Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.