Chain-of-Thought Reasoning Without Prompting
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2402.10200
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Published
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90
Papers
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How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 33 -
BitDelta: Your Fine-Tune May Only Be Worth One Bit
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A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
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Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
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OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Paper • 2402.07456 • Published • 38 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 48 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
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Shortened LLaMA: A Simple Depth Pruning for Large Language Models
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TrustLLM: Trustworthiness in Large Language Models
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SliceGPT: Compress Large Language Models by Deleting Rows and Columns
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DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Paper • 2401.14196 • Published • 44 -
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
Paper • 2401.12070 • Published • 40 -
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 61 -
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 37 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 69 -
Language Modeling Is Compression
Paper • 2309.10668 • Published • 78 -
Qwen Technical Report
Paper • 2309.16609 • Published • 30 -
CodeFusion: A Pre-trained Diffusion Model for Code Generation
Paper • 2310.17680 • Published • 68 -
Extending LLMs' Context Window with 100 Samples
Paper • 2401.07004 • Published • 14 -
The Impact of Reasoning Step Length on Large Language Models
Paper • 2401.04925 • Published • 15 -
Mixtral of Experts
Paper • 2401.04088 • Published • 152 -
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Paper • 2312.03818 • Published • 31 -
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Paper • 2312.04474 • Published • 27 -
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Paper • 2311.13600 • Published • 41 -
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Paper • 2311.00059 • Published • 17 -
CodePlan: Repository-level Coding using LLMs and Planning
Paper • 2309.12499 • Published • 68 -
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 22 -
Agents: An Open-source Framework for Autonomous Language Agents
Paper • 2309.07870 • Published • 39 -
Direct Language Model Alignment from Online AI Feedback
Paper • 2402.04792 • Published • 25 -
Rethinking Interpretability in the Era of Large Language Models
Paper • 2402.01761 • Published • 18 -
PDFTriage: Question Answering over Long, Structured Documents
Paper • 2309.08872 • Published • 51 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 74 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 135 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 26 -
Understanding LLMs: A Comprehensive Overview from Training to Inference
Paper • 2401.02038 • Published • 59 -
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
Paper • 2401.01325 • Published • 24 -
A Comprehensive Study of Knowledge Editing for Large Language Models
Paper • 2401.01286 • Published • 15 -
Time is Encoded in the Weights of Finetuned Language Models
Paper • 2312.13401 • Published • 18 -
TinyGSM: achieving >80% on GSM8k with small language models
Paper • 2312.09241 • Published • 33 -
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Paper • 2312.06674 • Published • 5 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 78 -
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Paper • 2311.13231 • Published • 25 -
Exponentially Faster Language Modelling
Paper • 2311.10770 • Published • 117 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 68 -
Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster
Paper • 2311.08263 • Published • 14 -
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Paper • 2402.10176 • Published • 32 -
Generative Representational Instruction Tuning
Paper • 2402.09906 • Published • 50 -
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Paper • 2310.08491 • Published • 48 -
Tuna: Instruction Tuning using Feedback from Large Language Models
Paper • 2310.13385 • Published • 8 -
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Paper • 2310.12823 • Published • 33 -
LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B
Paper • 2310.20624 • Published • 12 -
Learning From Mistakes Makes LLM Better Reasoner
Paper • 2310.20689 • Published • 24 -
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 27 -
Levels of AGI: Operationalizing Progress on the Path to AGI
Paper • 2311.02462 • Published • 30 -
Can LLMs Follow Simple Rules?
Paper • 2311.04235 • Published • 9 -
LLaMA Pro: Progressive LLaMA with Block Expansion
Paper • 2401.02415 • Published • 50 -
A Zero-Shot Language Agent for Computer Control with Structured Reflection
Paper • 2310.08740 • Published • 14 -
Premise Order Matters in Reasoning with Large Language Models
Paper • 2402.08939 • Published • 23 -
More Agents Is All You Need
Paper • 2402.05120 • Published • 46 -
Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache
Paper • 2401.02669 • Published • 10 -
Supervised Knowledge Makes Large Language Models Better In-context Learners
Paper • 2312.15918 • Published • 8 -
Instruction-tuning Aligns LLMs to the Human Brain
Paper • 2312.00575 • Published • 10 -
Prompt Engineering a Prompt Engineer
Paper • 2311.05661 • Published • 18 -
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Paper • 2311.02303 • Published • 4 -
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Paper • 2311.02262 • Published • 9 -
Personas as a Way to Model Truthfulness in Language Models
Paper • 2310.18168 • Published • 5 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 72 -
Customizing Language Model Responses with Contrastive In-Context Learning
Paper • 2401.17390 • Published -
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
Paper • 2402.07827 • Published • 43 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 15 -
Specialized Language Models with Cheap Inference from Limited Domain Data
Paper • 2402.01093 • Published • 45 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 24 -
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Paper • 2402.05140 • Published • 18 -
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Paper • 2310.19019 • Published • 9 -
Language Models can be Logical Solvers
Paper • 2311.06158 • Published • 14 -
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
Paper • 2311.12022 • Published • 22 -
Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 16 -
Digital Socrates: Evaluating LLMs through explanation critiques
Paper • 2311.09613 • Published • 1 -
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Paper • 2401.02500 • Published • 1 -
In-Context Principle Learning from Mistakes
Paper • 2402.05403 • Published • 12 -
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 20 -
Data Engineering for Scaling Language Models to 128K Context
Paper • 2402.10171 • Published • 18 -
A Closer Look at the Limitations of Instruction Tuning
Paper • 2402.05119 • Published • 5 -
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Paper • 2402.04858 • Published • 13 -
Code Representation Learning At Scale
Paper • 2402.01935 • Published • 12