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Found 6 papers, 2 papers with code
The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think
Long chain-of-thought (CoT) is an essential ingredient in effective usage of modern large language models, but our understanding of the reasoning strategies underlying these capabilities remains limited.
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work.
Efficient Long Context Language Model Retrieval with Compression
Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potential to surpass traditional sparse and dense retrieval methods.
Rethinking Code Refinement: Learning to Judge Code Efficiency
Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes.
LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs
Text-to-SQL models are pivotal for making Electronic Health Records (EHRs) accessible to healthcare professionals without SQL knowledge.
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks
Many existing works tackle this problem by generating synthetic data from the training data and then training models on them, recently using Large Language Models (LLMs).
