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 1051–1075 of 1135 papers

TitleStatusHype
Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's NestCode0
Alignment-based compositional semantics for instruction followingCode0
Iterative Label Refinement Matters More than Preference Optimization under Weak SupervisionCode0
From Loops to Oops: Fallback Behaviors of Language Models Under UncertaintyCode0
On the Loss of Context-awareness in General Instruction Fine-tuningCode0
IWISDM: Assessing instruction following in multimodal models at scaleCode0
CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing ConstraintsCode0
Cross-lingual Transfer of Reward Models in Multilingual AlignmentCode0
Mapping Instructions to Actions in 3D Environments with Visual Goal PredictionCode0
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation PredictionCode0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
RefuteBench: Evaluating Refuting Instruction-Following for Large Language ModelsCode0
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?Code0
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language ModelsCode0
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMsCode0
MDCure: A Scalable Pipeline for Multi-Document Instruction-FollowingCode0
ReIFE: Re-evaluating Instruction-Following EvaluationCode0
Re-Imagining Multimodal Instruction Tuning: A Representation ViewCode0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
SOTOPIA-Ω: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social AgentsCode0
WangchanLion and WangchanX MRC EvalCode0
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional AnalysisCode0
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual InstructionsCode0
Optimal Transport-Based Token Weighting scheme for Enhanced Preference OptimizationCode0
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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