A new fault-injection framework enables a systematic empirical study that produces 17 takeaways on error propagation in LLM inference and four software-only mitigation directions.
Xcopa: A multilingual dataset for causal commonsense reasoning
4 Pith papers cite this work. Polarity classification is still indexing.
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A literature survey that introduces a taxonomy for LLM reasoning paradigms, analyzes methodological trends, and synthesizes failure modes from over 300 papers.
A 72GB Tibetan corpus enables continual pre-training of Qwen2.5-7B and a 50B-A10B MoE model, with new benchmarks showing outperformance over prior Tibetan models.
Distillation and quantization expand the Apertus 8B LLM into a family of models up to 4B parameters with claimed strong accuracy and cost efficiency.
citing papers explorer
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Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
A new fault-injection framework enables a systematic empirical study that produces 17 takeaways on error propagation in LLM inference and four software-only mitigation directions.
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The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
A literature survey that introduces a taxonomy for LLM reasoning paradigms, analyzes methodological trends, and synthesizes failure modes from over 300 papers.
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From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan
A 72GB Tibetan corpus enables continual pre-training of Qwen2.5-7B and a 50B-A10B MoE model, with new benchmarks showing outperformance over prior Tibetan models.
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Apertus LLM Family Expansion via Distillation and Quantization
Distillation and quantization expand the Apertus 8B LLM into a family of models up to 4B parameters with claimed strong accuracy and cost efficiency.