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 1026–1050 of 1135 papers

TitleStatusHype
Instruction Makes a DifferenceCode0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
Automated curriculum generation for Policy Gradients from DemonstrationsCode0
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, ToxicityCode0
Adversarial Moment-Matching Distillation of Large Language ModelsCode0
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction TuningCode0
Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language ModelsCode0
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language ModelsCode0
Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model EvaluationCode0
Do LLMs estimate uncertainty well in instruction-following?Code0
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following TasksCode0
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution BehaviorsCode0
WildIFEval: Instruction Following in the WildCode0
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling EvaluatorsCode0
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across familiesCode0
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their VulnerabilitiesCode0
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model ReasoningCode0
Token-Efficient Leverage Learning in Large Language ModelsCode0
Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language ModelsCode0
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure DomainsCode0
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal TransportCode0
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language ModelsCode0
Evaluating the Instruction-following Abilities of Language Models using Knowledge TasksCode0
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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