Who cites «STaR: Bootstrapping Reasoning With Reasoning» · page 5

585 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

O1 Replication Journey: A Strategic Progress Report -- Part 1 — 2024 · arXiv:2410.18982
On the Diversity of Synthetic Data and its Impact on Training Large Language Models — 2024 · arXiv:2410.15226
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset — 2024 · arXiv:2402.10176
OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models — 2024 · arXiv:2410.09671
Optimizing Multimodal Large Language Models for Detection of Alcohol Advertisements via Adaptive Prompting — 2024 · Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security — 2024 · arXiv:2401.05459
Planning In Natural Language Improves LLM Search For Code Generation — 2024 · arXiv:2409.03733
Preference Optimization for Reasoning with Pseudo Feedback — 2024 · arXiv:2411.16345
Progress or Regress? Self-Improvement Reversal in Post-training — 2024 · arXiv:2407.05013
Prompt Engineering a Prompt Engineer — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
Quiet-STaR: Language models can teach themselves to think before speaking — 2024
RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation — 2024 · arXiv:2403.05313
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold — 2024 · arXiv:2406.14532
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Re-ReST: Reflection-Reinforced Self-Training for Language Agents — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search — 2024 · arXiv:2406.03816
Recursive Introspection: Teaching Language Model Agents How to Self-Improve — 2024 · arXiv:2407.18219
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? — 2024 · arXiv:2402.18272
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning — 2024 · arXiv:2410.08146
S3Prompt: Instructing the Model with Self-calibration, Self-recall and Self-aggregation to Improve In-context Learning — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models — 2024 · arXiv:2408.15565
STaR-GATE: Teaching Language Models to Ask Clarifying Questions — 2024 · arXiv:2403.19154
Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play — 2024 · arXiv:2411.00062
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters — 2024 · arXiv:2408.03314
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective — 2024 · arXiv:2412.14135
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering — 2024 · arXiv:2411.11504
Self-Data Distillation for Recovering Quality in Pruned Large Language Models — 2024 · arXiv:2410.09982
Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Self-Improvement in Language Models: The Sharpening Mechanism — 2024 · arXiv:2412.01951
Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels — 2024 · arXiv:2404.14313
Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Self-training Language Models for Arithmetic Reasoning — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Small Language Models Need Strong Verifiers to Self-Correct Reasoning — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling — 2024 · arXiv:2408.16737
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs — 2024 · arXiv:2412.08347
SocREval: Large Language Models with the Socratic Method for Reference-free Reasoning Evaluation — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows — 2024 · arXiv:2403.11322
Stream of Search (SoS): Learning to Search in Language — 2024 · arXiv:2404.03683
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization — 2024 · arXiv:2412.21037
Teaching Large Language Models to Reason with Reinforcement Learning — 2024 · arXiv:2403.04642
TextGrad: Automatic "Differentiation" via Text — 2024 · arXiv:2406.07496
The Llama 3 Herd of Models — 2024 · arXiv:2407.21783
The pitfalls of next-token prediction — 2024 · arXiv:2403.06963
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing — 2024 · arXiv:2404.12253
Training Language Models to Self-Correct via Reinforcement Learning — 2024 · arXiv:2409.12917
Tulu 3: Pushing Frontiers in Open Language Model Post-Training — 2024 · arXiv:2411.15124
V-STaR: Training Verifiers for Self-Taught Reasoners — 2024 · arXiv:2402.06457
Weak-to-Strong Reasoning — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models — 2024 · arXiv:2405.19262
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs — 2024 · Transactions of the Association for Computational Linguistics
Word Sense Disambiguation as a Game of Neurosymbolic Darts — 2024 · Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
面向小规模大语言模型推理优化的推理路径排序方法(A Reasoning Paths Ranking Method for Reasoning Optimization of Small-scale Large Language Models) — 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity — 2023 · arXiv:2302.04023
Alfred: A System for Prompted Weak Supervision — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models — 2023 · arXiv:2308.10379
Aligning Language Models with Preferences through f-divergence Minimization — 2023 · arXiv:2302.08215
Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training — 2023 · arXiv:2309.17179
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions — 2023 · arXiv:2307.13339
Answering Questions by Meta-Reasoning over Multiple Chains of Thought — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
AutoML-GPT: Automatic Machine Learning with GPT — 2023 · arXiv:2305.02499
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies — 2023 · arXiv:2308.03188
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models — 2023 · arXiv:2312.06585
Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society — 2023 · arXiv:2303.17760
CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Can GPT-3 Perform Statutory Reasoning? — 2023 · arXiv:2302.06100
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
Certified Deductive Reasoning with Language Models — 2023 · arXiv:2306.04031
Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Chain-of-Thought Reasoning is a Policy Improvement Operator — 2023 · arXiv:2309.08589
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Challenges and Applications of Large Language Models — 2023 · arXiv:2307.10169
ChemCrow: Augmenting large-language models with chemistry tools — 2023 · arXiv:2304.05376
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Cognitive Architectures for Language Agents — 2023 · arXiv:2309.02427
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Crystal: Introspective Reasoners Reinforced with Self-Feedback — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines — 2023 · arXiv:2310.03714
Democratizing Reasoning Ability: Tailored Learning from Large Language Model — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
Don''t Add, don''t Miss: Effective Content Preserving Generation from Pre-Selected Text Spans — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval — 2023 · arXiv:2309.15129
Evaluating Language Models for Mathematics through Interactions — 2023 · arXiv:2306.01694
Explain-then-translate: an analysis on improving program translation with self-generated explanations — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
Explanation Regeneration via Information Bottleneck — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Exploring the Effectiveness of Prompt Engineering for Legal Reasoning Tasks — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
Faithful Question Answering with Monte-Carlo Planning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Few Shot Rationale Generation using Self-Training with Dual Teachers — 2023 · Findings of the Association for Computational Linguistics: ACL 2023