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Found 8 papers, 5 papers with code
Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era.
Diver: Large Language Model Decoding with Span-Level Mutual Information Verification
Large language models (LLMs) have shown impressive capabilities in adapting to various tasks when provided with task-specific instructions.
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
In addition, we find that models tuned on cross-lingual instruction following samples can follow the instruction in the output language without further tuning.
Bridging the Gap between Different Vocabularies for LLM Ensemble
Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable.
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
One is a cascaded approach where outputs (tokens or states) of a separately trained speech recognition system are used as inputs for LLMs, which limits their potential in modeling alignment between speech and text.
Instance-aware Prompt Learning for Language Understanding and Generation
Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
Exploiting Curriculum Learning in Unsupervised Neural Machine Translation
Back-translation (BT) has become one of the de facto components in unsupervised neural machine translation (UNMT), and it explicitly makes UNMT have translation ability.
