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 751–800 of 1135 papers

TitleStatusHype
On Instruction-Finetuning Neural Machine Translation Models—0
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References—0
Superficial Safety Alignment Hypothesis—0
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe—0
CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing ConstraintsCode0
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation—0
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data PartitionsCode0
SAG: Style-Aligned Article Generation via Model Collaboration—0
Self-Powered LLM Modality Expansion for Large Speech-Text ModelsCode0
Video Instruction Tuning With Synthetic Data—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
LLaVA-Critic: Learning to Evaluate Multimodal Models—0
Better Instruction-Following Through Minimum Bayes Risk—0
The Perfect Blend: Redefining RLHF with Mixture of Judges—0
Revisiting the Superficial Alignment Hypothesis—0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following Benchmark—0
Inference-Time Language Model Alignment via Integrated Value Guidance—0
EAGLE: Towards Efficient Arbitrary Referring Visual Prompts Comprehension for Multimodal Large Language Models—0
Mitigating the Bias of Large Language Model EvaluationCode0
FMDLlama: Financial Misinformation Detection based on Large Language ModelsCode0
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment BenchmarkingCode0
Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language ModelsCode0
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation—0
CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks—0
SIFToM: Robust Spoken Instruction Following through Theory of Mind—0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models—0
SFR-RAG: Towards Contextually Faithful LLMs—0
Emo-DPO: Controllable Emotional Speech Synthesis through Direct Preference Optimization—0
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?—0
Keypoints-Integrated Instruction-Following Data Generation for Enhanced Human Pose Understanding in Multimodal Models—0
AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting LLMsCode0
RNR: Teaching Large Language Models to Follow Roles and Rules—0
Leveraging LLMs for Influence Path Planning in Proactive Recommendation—0
Continual Skill and Task Learning via Dialogue—0
Prompt Baking—0
Self-Judge: Selective Instruction Following with Alignment Self-EvaluationCode0
Language Models Benefit from Preparation with Elicited Knowledge—0
Does Alignment Tuning Really Break LLMs' Internal Confidence?Code0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis—0
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction TuningCode0
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora—0
Preference-Guided Reflective Sampling for Aligning Language ModelsCode0
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation—0
Can Large Language Models Understand Symbolic Graphics Programs?—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