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 651–700 of 1135 papers

TitleStatusHype
HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models—0
From Persona to Personalization: A Survey on Role-Playing Language Agents—0
URL: Universal Referential Knowledge Linking via Task-instructed Representation CompressionCode0
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language ModelsCode2
Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models—0
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist CollaborationCode2
Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following—0
The Instruction Hierarchy: Training LLMs to Prioritize Privileged InstructionsCode1
Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning—0
Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V—0
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning—0
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts—0
CodecLM: Aligning Language Models with Tailored Synthetic Data—0
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs—0
Facial Affective Behavior Analysis with Instruction TuningCode1
Teaching Llama a New Language Through Cross-Lingual Knowledge TransferCode0
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues—0
Evaluating LLMs at Detecting Errors in LLM ResponsesCode1
An Incomplete Loop: Deductive, Inductive, and Abductive Learning in Large Language Models—0
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language ModelsCode2
HyperCLOVA X Technical Report—0
Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language ReasoningCode0
LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction—0
Token-Efficient Leverage Learning in Large Language ModelsCode0
Direct Preference Optimization of Video Large Multimodal Models from Language Model RewardCode2
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPCode0
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks—0
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models—0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLMCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation—0
FlashFace: Human Image Personalization with High-fidelity Identity PreservationCode3
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language ModelsCode0
Argument Quality Assessment in the Age of Instruction-Following Large Language Models—0
WangchanLion and WangchanX MRC EvalCode0
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning—0
Building Accurate Translation-Tailored LLMs with Language Aware Instruction TuningCode0
Improving the Robustness of Large Language Models via Consistency Alignment—0
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
RewardBench: Evaluating Reward Models for Language ModelingCode4
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
VisualCritic: Making LMMs Perceive Visual Quality Like Humans—0
Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language ModelsCode2
Third-Party Language Model Performance Prediction from InstructionCode0
MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World ControlCode2
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning—0
ChartInstruct: Instruction Tuning for Chart Comprehension and ReasoningCode1
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language ModelCode2
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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