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 • 6 -
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 • 37 -
Instruction Following without Instruction Tuning
Paper • 2409.14254 • Published • 14 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 119 -
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 -
Is In-Context Learning Sufficient for Instruction Following in LLMs?
Paper • 2405.19874 • Published -
Implicit In-context Learning
Paper • 2405.14660 • Published -
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
Paper • 2407.02028 • Published -
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
Paper • 2405.15115 • Published -
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 -
MLPs Learn In-Context on Regression and Classification Tasks
Paper • 2405.15618 • Published -
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
Paper • 2403.06326 • Published • 1