INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Paper
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2306.04757
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Published
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6
Instructions
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Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
Paper • 2308.01240 • Published • 2 -
Can Large Language Models Understand Real-World Complex Instructions?
Paper • 2309.09150 • Published • 2 -
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection
Paper • 2308.10819 • Published -
InFoBench: Evaluating Instruction Following Ability in Large Language Models
Paper • 2401.03601 • Published • 7 -
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
Paper • 2310.20410 • Published • 1 -
Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning
Paper • 2303.10475 • Published • 2 -
Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?
Paper • 2402.11597 • Published • 1 -
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data
Paper • 2407.03942 • Published -
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Paper • 2409.16191 • Published • 41 -
Instruction Following without Instruction Tuning
Paper • 2409.14254 • Published • 27 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 133 -
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Paper • 2312.01552 • Published • 30 -
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
Paper • 2409.13928 • Published • 1 -
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Paper • 2404.07129 • Published • 3 -
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
Paper • 2403.06326 • Published • 1 -
Batch Prompting: Efficient Inference with Large Language Model APIs
Paper • 2301.08721 • Published • 1 -
Finetuned Language Models Are Zero-Shot Learners
Paper • 2109.01652 • Published • 2 -
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models
Paper • 2409.18943 • Published • 26 -
Can Models Learn Skill Composition from Examples?
Paper • 2409.19808 • Published • 8 -
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
Paper • 2410.01560 • Published • 3 -
Not All LLM Reasoners Are Created Equal
Paper • 2410.01748 • Published • 27 -
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization
Paper • 2410.04717 • Published • 17 -
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Paper • 2410.09335 • Published • 14 -
Baichuan Alignment Technical Report
Paper • 2410.14940 • Published • 47