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

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Showing 701–750 of 1135 papers

TitleStatusHype
Fox-1 Technical Report—0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Multi-Reward as Condition for Instruction-based Image Editing—0
On the Loss of Context-awareness in General Instruction Fine-tuningCode0
Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language ModelsCode0
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors—0
TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models—0
Beyond Content Relevance: Evaluating Instruction Following in Retrieval ModelsCode0
MDCure: A Scalable Pipeline for Multi-Document Instruction-FollowingCode0
UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function—0
FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization systemCode0
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models—0
BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning—0
Unbounded: A Generative Infinite Game of Character Life Simulation—0
Cross-lingual Transfer of Reward Models in Multilingual AlignmentCode0
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks—0
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
Large Language Models for Autonomous Driving (LLM4AD): Concept, Benchmark, Experiments, and Challenges—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
Do LLMs estimate uncertainty well in instruction-following?Code0
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation—0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
Evaluating the Instruction-following Abilities of Language Models using Knowledge TasksCode0
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference OptimizationCode0
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding—0
Improving Instruction-Following in Language Models through Activation Steering—0
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling—0
ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization—0
DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model—0
Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search—0
Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs—0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
Thinking LLMs: General Instruction Following with Thought Generation—0
Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles—0
Surgical-LLaVA: Toward Surgical Scenario Understanding via Large Language and Vision Models—0
Are You Human? An Adversarial Benchmark to Expose LLMs—0
SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins—0
Nudging: Inference-time Alignment of LLMs via Guided Decoding—0
Evolutionary Contrastive Distillation for Language Model Alignment—0
Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy—0
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints—0
Self-Boosting Large Language Models with Synthetic Preference Data—0
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding—0
Large Language Model Compression with Neural Architecture Search—0
ReIFE: Re-evaluating Instruction-Following EvaluationCode0
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
Multimodal Situational Safety—0
TOWER: Tree Organized Weighting for Evaluating Complex Instructions—0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization—0
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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