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Domain-adapting LLMs for EDA requires explicit RAG scenario training to prevent performance degradation, and QA augmentation during corpus construction further boosts performance.
Key contribution not extracted.
Stop chasing leaderboard gains on generic benchmarks: PJB reveals that domain-specific weaknesses in person-job retrieval far outweigh the benefits of general model upgrades, and that query understanding modules can actually hurt performance.
Ditch static embeddings: Generative retrieval, powered by reinforcement learning, lets models dynamically reason about relevance, outperforming larger contrastively-trained models on reasoning-intensive tasks.
