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 1076–1100 of 1135 papers

TitleStatusHype
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following—0
FILM: Following Instructions in Language with Modular MethodsCode1
Waypoint Models for Instruction-guided Navigation in Continuous EnvironmentsCode1
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
Lexicon Learning for Few Shot Sequence ModelingCode1
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
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring ExpressionCode1
Lexicon Learning for Few-Shot Neural Sequence ModelingCode1
Zero-shot Task Adaptation using Natural Language—0
Generalization in Instruction Following Systems—0
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following TasksCode0
PanGEA: The Panoramic Graph Environment Annotation Toolkit—0
A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environmentCode1
Are We There Yet? Learning to Localize in Embodied Instruction Following—0
Factorizing Perception and Policy for Interactive Instruction FollowingCode1
Spatial Language Understanding for Object Search in Partially Observed City-scale EnvironmentsCode0
From “Before” to “After”: Generating Natural Language Instructions from Image Pairs in a Simple Visual Domain—0
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction FollowingCode1
RMM: A Recursive Mental Model for Dialogue NavigationCode1
Modular Networks for Compositional Instruction Following—0
Learning to Recombine and Resample Data for Compositional GeneralizationCode0
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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