yicui
's Collections
Instructions
updated
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large
Language Models
Paper
•
2306.04757
•
Published
•
6
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
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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
•
135
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
•
27
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
•
15
Baichuan Alignment Technical Report
Paper
•
2410.14940
•
Published
•
48
Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper
•
2212.10560
•
Published
•
8