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Small language models need strong verifiers to self-correct reasoning
Self-correction has emerged as a promising solution to boost the reasoning performance of
large language models (LLMs), where LLMs refine their solutions using self-generated …
large language models (LLMs), where LLMs refine their solutions using self-generated …
TRACE the evidence: Constructing knowledge-grounded reasoning chains for retrieval-augmented generation
Retrieval-augmented generation (RAG) offers an effective approach for addressing question
answering (QA) tasks. However, the imperfections of the retrievers in RAG models often …
answering (QA) tasks. However, the imperfections of the retrievers in RAG models often …
Graph-constrained reasoning: Faithful reasoning on knowledge graphs with large language models
Large language models (LLMs) have demonstrated impressive reasoning abilities, but they
still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address …
still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address …
Demystifying chains, trees, and graphs of thoughts
The field of natural language processing (NLP) has witnessed significant progress in recent
years, with a notable focus on improving large language models'(LLM) performance through …
years, with a notable focus on improving large language models'(LLM) performance through …
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
Offline evaluation of LLMs is crucial in understanding their capacities, though current
methods remain underexplored in existing research. In this work, we focus on the offline …
methods remain underexplored in existing research. In this work, we focus on the offline …
Confidence Improves Self-Consistency in LLMs
Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling
diverse reasoning paths and selecting the most frequent answer. However, it is …
diverse reasoning paths and selecting the most frequent answer. However, it is …
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models
Large Multimodal Models (LMMs) have achieved strong performance across a range of
vision and language tasks. However, their spatial reasoning capabilities are under …
vision and language tasks. However, their spatial reasoning capabilities are under …
[PDF][PDF] Knowledge Graph and Large Language Model Co-learning via Structure-oriented Retrieval Augmented Generation
Recent years have witnessed major technical breakthroughs in AI–facilitated by tremendous
data and high-performance computers, large language models (LLMs) have brought …
data and high-performance computers, large language models (LLMs) have brought …