4 papers
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users assess the correctness of gene…
Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as factually consistent summar…
The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
Yuxuan Ye, Jun Han, Ao Hu +7
End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgents, FinAgent, QuantAgent, and F…
Quantifying Compositionality of Classic and State-of-the-Art Embeddings
Zhijin Guo, Chenhao Xue, Zhaozhen Xu +4
For language models to generalize correctly to novel expressions, it is critical that they exploit access compositional meanings when this is justified. Even if we don't know what…