https://github.com/lrudl/sad
공식 코드Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Papers with Code 아카이브
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/lsh0520/3d-molm
공식 코드https://github.com/ltu-machine-learning/idocvqa
공식 코드https://github.com/lujunru/rolemrc
공식 코드RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
https://github.com/lukeming-tsinghua/instruction-tuning-for-open-world-ie
공식 코드PIVOINE: Instruction Tuning for Open-world Information Extraction
https://github.com/lunyiliu/coachlm
공식 코드CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
https://github.com/luodian/otter
공식 코드Otter: A Multi-Modal Model with In-Context Instruction Tuning
https://github.com/luohongyin/langcode
공식 코드Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
https://github.com/lxq1000/dfa-instruct
공식 코드https://github.com/lzw108/emollms
공식 코드https://github.com/lzw108/fmd
공식 코드FMDLlama: Financial Misinformation Detection based on Large Language Models
https://github.com/magicgh/self-map
공식 코드On the Multi-turn Instruction Following for Conversational Web Agents
https://github.com/malusamayo/underspec-analysis
공식 코드What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts
https://github.com/mao1207/BioMed-VITAL
공식 코드Biomedical Visual Instruction Tuning with Clinician Preference Alignment
https://github.com/mathieutuli/ltl-gata
공식 코드https://github.com/mbzuai-nlp/bactrian-x
공식 코드Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation
https://github.com/mbzuai-oryx/geochat
공식 코드GeoChat: Grounded Large Vision-Language Model for Remote Sensing
https://github.com/mcgill-nlp/instruct-qa
공식 코드Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
https://github.com/meigen-ai/multitalk
공식 코드Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation
https://github.com/meowpass/followcomplexinstruction
공식 코드https://github.com/meowpass/pbif
공식 코드Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
https://github.com/microsoft/llm-data-creation
공식 코드https://github.com/microsoft/lmops
공식 코드https://github.com/microsoft/recai
공식 코드RecExplainer: Aligning Large Language Models for Explaining Recommendation Models
https://github.com/microsoft/unilm
공식 코드https://github.com/mightyzau/infmllm
공식 코드https://github.com/milkthink-lab/routereval
공식 코드RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
https://github.com/mimasss2/otpo
공식 코드Optimal Transport-Based Token Weighting scheme for Enhanced Preference Optimization
https://github.com/mingliiii/cherry_llm
공식 코드https://github.com/mingliiii/gradient_unified
공식 코드https://github.com/mingliiii/ruler
공식 코드RuleR: Improving LLM Controllability by Rule-based Data Recycling
https://github.com/minnesotanlp/select-llm
공식 코드SelectLLM: Can LLMs Select Important Instructions to Annotate?
https://github.com/miulab/instupr
공식 코드InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models
https://github.com/mivg/fallbacks
공식 코드From Loops to Oops: Fallback Behaviors of Language Models Under Uncertainty
https://github.com/ml-ailab/ophglm
공식 코드https://github.com/mlfoundations/VisIT-Bench
공식 코드VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.