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

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Showing 676–700 of 1135 papers

TitleStatusHype
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPCode0
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks—0
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models—0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLMCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation—0
FlashFace: Human Image Personalization with High-fidelity Identity PreservationCode3
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language ModelsCode0
Argument Quality Assessment in the Age of Instruction-Following Large Language Models—0
WangchanLion and WangchanX MRC EvalCode0
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning—0
Building Accurate Translation-Tailored LLMs with Language Aware Instruction TuningCode0
Improving the Robustness of Large Language Models via Consistency Alignment—0
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
RewardBench: Evaluating Reward Models for Language ModelingCode4
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
VisualCritic: Making LMMs Perceive Visual Quality Like Humans—0
Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language ModelsCode2
Third-Party Language Model Performance Prediction from InstructionCode0
MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World ControlCode2
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning—0
ChartInstruct: Instruction Tuning for Chart Comprehension and ReasoningCode1
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language ModelCode2
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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