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 1026–1050 of 1135 papers

TitleStatusHype
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—0
Instruction Tuning with GPT-4Code4
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and FeedbackCode1
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model SocietyCode6
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models—0
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init AttentionCode5
Large Language Model Instruction Following: A Survey of Progresses and ChallengesCode2
Lana: A Language-Capable Navigator for Instruction Following and GenerationCode1
CB2: Collaborative Natural Language Interaction Research PlatformCode1
ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmarkCode1
Alexa Arena: A User-Centric Interactive Platform for Embodied AICode1
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw DatasetCode0
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction FollowingCode1
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt InjectionCode4
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
"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared AutonomyCode1
Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following—0
Precise Zero-Shot Dense Retrieval without Relevance LabelsCode2
Self-Instruct: Aligning Language Models with Self-Generated InstructionsCode5
Continual Learning for Instruction Following from Realtime FeedbackCode0
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot ReasoningCode1
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action CorrectionsCode1
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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