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 951–1000 of 1135 papers

TitleStatusHype
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn DialogueCode2
AgentBench: Evaluating LLMs as AgentsCode4
Toward Zero-Shot Instruction FollowingCode0
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question AnsweringCode1
ETHER: Aligning Emergent Communication for Hindsight Experience Replay—0
L-Eval: Instituting Standardized Evaluation for Long Context Language ModelsCode6
Instruction-following Evaluation through Verbalizer Manipulation—0
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsCode2
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?—0
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning—0
AlpaGasus: Training A Better Alpaca with Fewer DataCode1
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMsCode2
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study—0
MMBench: Is Your Multi-modal Model an All-around Player?Code5
Instruction Mining: Instruction Data Selection for Tuning Large Language Models—0
Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorsCode1
Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning—0
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?Code2
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language ModelsCode1
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control—0
KITE: Keypoint-Conditioned Policies for Semantic Manipulation—0
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
On the Exploitability of Instruction TuningCode1
OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and DialogueCode1
BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language ModelsCode2
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation—0
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language ModelsCode2
MiniLLM: Knowledge Distillation of Large Language ModelsCode2
Valley: Video Assistant with Large Language model Enhanced abilitYCode2
How Can Recommender Systems Benefit from Large Language Models: A SurveyCode3
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open ResourcesCode4
"Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Dataset—0
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayCode4
STEVE-1: A Generative Model for Text-to-Behavior in MinecraftCode2
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User InterfacesCode1
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
Controllable Text-to-Image Generation with GPT-4—0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language ModelsCode2
PandaGPT: One Model To Instruction-Follow Them AllCode2
TOAST: Transfer Learning via Attention SteeringCode1
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of PathologyCode1
PIVOINE: Instruction Tuning for Open-world Information ExtractionCode1
ExpertPrompting: Instructing Large Language Models to be Distinguished ExpertsCode2
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank AdaptationCode1
SAIL: Search-Augmented Instruction Learning—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
QLoRA: Efficient Finetuning of Quantized LLMsCode6
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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