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 1051–1100 of 1135 papers

TitleStatusHype
Instruction Mining: Instruction Data Selection for Tuning Large Language Models—0
Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning—0
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control—0
KITE: Keypoint-Conditioned Policies for Semantic Manipulation—0
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation—0
"Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Dataset—0
Controllable Text-to-Image Generation with GPT-4—0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
SAIL: Search-Augmented Instruction Learning—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance—0
Multimodal Web Navigation with Instruction-Finetuned Foundation Models—0
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach—0
Knowledge-enhanced Agents for Interactive Text Games—0
Accessible Instruction-Following Agent—0
Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models—0
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents—0
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model—0
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—0
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models—0
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw DatasetCode0
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation—0
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks—0
Distilling Internet-Scale Vision-Language Models into Embodied Agents—0
Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following—0
Continual Learning for Instruction Following from Realtime FeedbackCode0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
UGIF: UI Grounded Instruction Following—0
Learning to Follow Instructions in Text-Based GamesCode0
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following—0
Don't Copy the Teacher: Data and Model Challenges in Embodied DialogueCode0
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning—0
Iterative Vision-and-Language Navigation—0
Language Models are General-Purpose Interfaces—0
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction FollowingCode0
Summarizing a virtual robot's past actions in natural language—0
Compositionality as Lexical SymmetryCode0
Less is More: Generating Grounded Navigation Instructions from Landmarks—0
Explicit Object Relation Alignment for Vision and Language Navigation—0
Skill Induction and Planning with Latent Language—0
Compositional Data and Task Augmentation for Instruction Following—0
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following—0
Hierarchical Modular Framework for Long Horizon Instruction FollowingCode0
Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language—0
Analysis of Language Change in Collaborative Instruction FollowingCode0
Modular Framework for Visuomotor Language Grounding—0
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning—0
Draw Me a Flower: Processing and Grounding Abstraction in Natural Language—0
Zero-shot Task Adaptation using Natural Language—0
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following TasksCode0
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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