Natural Language Understanding
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Benchmarks
Most implemented
Attention Is All You Need
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Learning Transferable Visual Models From Natural Language Supervision
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
A Neural Conversational Model
Cross-lingual Language Model Pretraining
Papers
From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, high cost, stigma, and privacy concerns, has created an urgent need for …
Natural Language UnderstandingLarge Language Models with At Most One Spike per Neuron
Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising path toward energy-efficient large language models (LLMs). Time-to-first-spike (TTFS) coding generates at most o…
Natural Language UnderstandingNL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry
Recent advances in large language models (LLMs) have demonstrated strong capabilities in natural language understanding and mathematical reasoning. However, their ability to translate informal mathematical problems into …
Natural Language UnderstandingMathematical ReasoningSymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers
Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reasoning, frequently suffering from hallucinations and inconsistency. Exi…
Natural Language UnderstandingLogical ReasoningLearning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation
We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting synthetic examples with a fixed reward threshold, our method formul…
Natural Language UnderstandingFact VerificationSPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands. Deploying smaller LLMs directly on the ed…
Natural Language Understanding