Jikun Wu
5 papers · Latest:
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
QD-LLM uses neuroevolution to evolve prompt embeddings, enabling diverse and high-quality LLM outputs without fine-tuning.
EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent
EvoPref, a multi-objective evolutionary algorithm, discovers diverse LLM alignments, overcoming preference collapse in gradient-based methods.
When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models
ReaLM-Retrieve adaptively injects evidence during reasoning, improving large model performance and efficiency by detecting knowledge gaps.
FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification
FinGround is a new pipeline that significantly reduces financial AI hallucination by verifying atomic claims against regulatory filings, crucial for compliance.
ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection
ComplianceNLP is a knowledge-graph-augmented RAG system for automated regulatory gap detection, outperforming GPT-4o and improving analyst efficiency.
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