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Instruction Following

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보존된 평가 기록: 4

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/chenzhongwu20/rulerag_icl_ft

공식 코드

RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering

https://github.com/chiangel/awesome-llm-for-recsys

공식 코드

How Can Recommender Systems Benefit from Large Language Models: A Survey

https://github.com/chtmp223/suri

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Suri: Multi-constraint Instruction Following for Long-form Text Generation

https://github.com/clic-lab/ciff

공식 코드

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

https://github.com/clic-lab/drif

공식 코드

Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction

https://github.com/clinicalml/co-llm

공식 코드

Learning to Decode Collaboratively with Multiple Language Models

https://github.com/coastalcph/zeroshot_lexglue

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ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark

https://github.com/columbia-nlp-lab/lionalignment

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LIONs: An Empirically Optimized Approach to Align Language Models

https://github.com/comeandcode/MRT

공식 코드

Re-Imagining Multimodal Instruction Tuning: A Representation View

https://github.com/cone-mt/benchmax

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BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models

https://github.com/coniferlm/conifer

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Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models

https://github.com/cuishiyao96/fft

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FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity

https://github.com/cwang621/blsp-emo

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BLSP-Emo: Towards Empathetic Large Speech-Language Models

https://github.com/damo-nlp-sg/seallms

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SeaLLMs -- Large Language Models for Southeast Asia

https://github.com/dandelionsllm/pandallm

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Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models

https://github.com/danielfperez/castillo

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CASTILLO: Characterizing Response Length Distributions of Large Language Models

https://github.com/dcdmllm/align2llava

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Align$^2$LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation

https://github.com/dcdmllm/cheetah

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Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions

https://github.com/declare-lab/instraug

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Towards Robust Instruction Tuning on Multimodal Large Language Models

https://github.com/declare-lab/resta

공식 코드

Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic

https://github.com/deepmodeling/crystalformer

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CrystalFormer-RL: Reinforcement Fine-Tuning for Materials Design

https://github.com/deepseek-ai/janus

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Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

https://github.com/dgaddy/environment-learning

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Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following

https://github.com/divraz/TaskSegmentation

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Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains

https://github.com/dongguanting/FollowRAG

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Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

https://github.com/dongyh20/chain-of-spot

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Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models

https://github.com/dptech-corp/Uni-SMART

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SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding

https://github.com/duykhuongnguyen/laser-mab

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LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits

https://github.com/dvlab-research/longlora

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LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

https://github.com/dxlong2000/FormatEval

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LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs

https://github.com/dynamofl/primeguard

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PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing

https://github.com/eason-li-ais/drafterbench

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DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering

https://github.com/ekinakyurek/compgen

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Learning to Recombine and Resample Data for Compositional Generalization

https://github.com/ekinakyurek/lexical

공식 코드

Lexicon Learning for Few-Shot Neural Sequence Modeling

https://github.com/ekinakyurek/lexsym

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Compositionality as Lexical Symmetry

https://github.com/ekwinox117/multi-challenge

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MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs

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