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 551–575 of 1135 papers

TitleStatusHype
Self-Powered LLM Modality Expansion for Large Speech-Text ModelsCode0
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference OptimizationCode0
Language as an Abstraction for Hierarchical Deep Reinforcement LearningCode0
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure DomainsCode0
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLMCode0
Preference-Guided Reflective Sampling for Aligning Language ModelsCode0
Unintended Impacts of LLM Alignment on Global RepresentationCode0
Pre-Learning Environment Representations for Data-Efficient Neural Instruction FollowingCode0
Generative Visual Instruction TuningCode0
Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics DomainsCode0
Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax ReductionCode0
PrimeGuard: Safe and Helpful LLMs through Tuning-Free RoutingCode0
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction FollowingCode0
Instruction Makes a DifferenceCode0
ProgCo: Program Helps Self-Correction of Large Language ModelsCode0
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language ModelsCode0
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-SteeringCode0
Grade Score: Quantifying LLM Performance in Option SelectionCode0
Building Accurate Translation-Tailored LLMs with Language Aware Instruction TuningCode0
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation LearningCode0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following TasksCode0
Analysis of Language Change in Collaborative Instruction FollowingCode0
Multi-Level Compositional Reasoning for Interactive Instruction FollowingCode0
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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