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 201–225 of 1135 papers

TitleStatusHype
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code GenerationCode1
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model ReasoningCode0
Rank1: Test-Time Compute for Reranking in Information RetrievalCode2
URO-Bench: A Comprehensive Benchmark for End-to-End Spoken Dialogue Models—0
ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Models—0
Order Matters: Investigate the Position Bias in Multi-constraint Instruction FollowingCode0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
Capability Instruction Tuning: A New Paradigm for Dynamic LLM RoutingCode0
NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot InteractionCode0
Sequence-level Large Language Model Training with Contrastive Preference Optimization—0
SOTOPIA-Ω: Dynamic Strategy Injection Learning and Social Instruction Following Evaluation for Social AgentsCode0
StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction FollowingCode1
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment—0
Investigating Non-Transitivity in LLM-as-a-Judge—0
Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh—0
TESS 2: A Large-Scale Generalist Diffusion Language ModelCode2
MMTEB: Massive Multilingual Text Embedding Benchmark—0
TALKPLAY: Multimodal Music Recommendation with Large Language Models—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models—0
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-FollowingCode0
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models—0
Step-Audio: Unified Understanding and Generation in Intelligent Speech InteractionCode7
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding—0
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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