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 451–475 of 1135 papers

TitleStatusHype
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing—0
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling—0
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding—0
AC/DC: LLM-based Audio Comprehension via Dialogue Continuation—0
Better Instruction-Following Through Minimum Bayes Risk—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective—0
Knowledge-enhanced Agents for Interactive Text Games—0
Differential Information: An Information-Theoretic Perspective on Preference Optimization—0
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following—0
DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment—0
Improving the Robustness of Large Language Models via Consistency Alignment—0
Improving Reward Models with Synthetic Critiques—0
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty—0
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets—0
Improving Instruct Models for Free: A Study on Partial Adaptation—0
Improving Instruction-Following in Language Models through Activation Steering—0
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning—0
Benchmarking and Improving Generator-Validator Consistency of Language Models—0
Alzheimer's Dementia Detection Using Perplexity from Paired Large Language 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