SOTAVerified

Instruction Following

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 1135 papers

TitleStatusHype
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMsCode2
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation ModelCode2
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMsCode2
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward SystemsCode2
Rank1: Test-Time Compute for Reranking in Information RetrievalCode2
TESS 2: A Large-Scale Generalist Diffusion Language ModelCode2
mFollowIR: a Multilingual Benchmark for Instruction Following in RetrievalCode2
MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMsCode2
Critique Fine-Tuning: Learning to Critique is More Effective than Learning to ImitateCode2
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual FeedbackCode2
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual GroundingCode2
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
Open6DOR: Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based ApproachCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions FollowingCode2
Toward General Instruction-Following Alignment for Retrieval-Augmented GenerationCode2
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation DataCode2
DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning DataCode2
Robin3D: Improving 3D Large Language Model via Robust Instruction TuningCode2
OmniBench: Towards The Future of Universal Omni-Language ModelsCode2
Archon: An Architecture Search Framework for Inference-Time TechniquesCode2
SciLitLLM: How to Adapt LLMs for Scientific Literature UnderstandingCode2
Autonomous Improvement of Instruction Following Skills via Foundation ModelsCode2
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian LanguagesCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoIF (Llama3 70B)Inst-level loose-accuracy90.4—Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88—Unverified
3GPT-4Inst-level loose-accuracy85.37—Unverified
4PaLM 2 SInst-level loose-accuracy59.11—Unverified