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 726–750 of 1135 papers

TitleStatusHype
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents—0
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Unified Mind Model: Reimagining Autonomous Agents in the LLM Era—0
Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning—0
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators—0
Privately Aligning Language Models with Reinforcement Learning—0
Prompt Baking—0
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following—0
Unleashing Hour-Scale Video Training for Long Video-Language Understanding—0
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities—0
PUMGPT: A Large Vision-Language Model for Product Understanding—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis—0
Question: How do Large Language Models perform on the Question Answering tasks? Answer:—0
BAP v2: An Enhanced Task Framework for Instruction Following in Minecraft Dialogues—0
Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs—0
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents—0
Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models—0
Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model—0
RE-Adapt: Reverse Engineered Adaptation of Large Language Models—0
WatME: Towards Lossless Watermarking Through Lexical Redundancy—0
Accessible Instruction-Following Agent—0
RECAST: Strengthening LLMs' Complex Instruction Following with Constraint-Verifiable Data—0
RecExplainer: Aligning Large Language Models for Explaining Recommendation Models—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