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Found 6 papers, 3 papers with code
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints
Previous studies show that semantic parsing enhances the performance of smaller models (e. g., BERT) on downstream tasks.
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Finally, based on our unified perspective, we explore the challenges and future research directions for aligning large language models with human preferences.
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and Bridging
The rapid advancement of large language models has given rise to a plethora of applications across a myriad of real-world tasks, mainly centered on aligning with human intent.
PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought
Considering computational efficiency instead of allocating a dedicated LLM for prompt enhancement to each individual model or dataset we integrate adapters that facilitate dataset-specific adaptation leveraging a shared pre-trained LLM as the foundation for this process.
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e. g., coming up with the right arguments for calling routines), requiring a deeper comprehension of complex file interactions.
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning
Distantly-Supervised Named Entity Recognition (DS-NER) is widely used in real-world scenarios.
