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 801–850 of 1135 papers

TitleStatusHype
CROME: Cross-Modal Adapters for Efficient Multimodal LLM—0
IFShip: Interpretable Fine-grained Ship Classification with Domain Knowledge-Enhanced Vision-Language ModelsCode0
Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications—0
Creating Arabic LLM Prompts at Scale—0
LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial DescriptionCode0
Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection—0
EXAONE 3.0 7.8B Instruction Tuned Language Model—0
A Framework for Fine-Tuning LLMs using Heterogeneous Feedback—0
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments—0
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language ModelsCode0
SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain—0
PrimeGuard: Safe and Helpful LLMs through Tuning-Free RoutingCode0
HAPFI: History-Aware Planning based on Fused Information—0
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—0
Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training ApproachCode0
Situated Instruction Following—0
Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual EnvironmentsCode0
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models—0
LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition—0
From Loops to Oops: Fallback Behaviors of Language Models Under UncertaintyCode0
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course—0
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data—0
Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLMCode0
Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets—0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting—0
DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment—0
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents—0
Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation—0
A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens—0
Following Length Constraints in Instructions—0
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization—0
AdaGrad under Anisotropic Smoothness—0
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
IWISDM: Assessing instruction following in multimodal models at scaleCode0
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought—0
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators—0
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal TransportCode0
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models—0
Refine Large Language Model Fine-tuning via Instruction Vector—0
Embodied Instruction Following in Unknown Environments—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment—0
Generative Visual Instruction TuningCode0
Grade Score: Quantifying LLM Performance in Option SelectionCode0
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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