EMNLP2025 Ghonim: concept-ediq a massive bank of concepts multi model semantically linked EMNLP2025 Bai: understanding and leveraging expert specialization of context faithfulness Two steps: step one is to use router tuning to prioritize experts that rely on context, step two is to especially hit those for fine-tuning for improved Qantas alliance. Big gainz and hot pot and other QA data set just by the router tuning EMNLP2025 Vasu: literature grounded hypothesis generation Use citation links to generate a Providence graph of hypothesis, then, fine tune a language model to reproduce this Providence graph, use resulting model to improve RAG that would be contextually grounded EMNLP2025Li: enhancing RAG RESPONSE evaluator Cast rag response evaluator as a reinforcement learning problem with accuracy, format, evidence use rewards. Perform reinforcement learning fine-tuning to improve metric EMNLP2025 Wang: understanding expressivity of language models through the next token distribution Language models have an inductive bias for low or very high entropy tokens where medium entropy are harder to illicit, this is because embedding is deficient in rank EMNLP2025 Arad: SAEs are good for steering if you select the right features There are some features that actually meaningfully preturb the output, intervening on those actually still work EMNLP2025 Zhang: a survey on hallucination A survey of hallucination