Two new research papers propose methods to improve the calibration of large language models (LLMs). The first paper introduces a framework based on Item Response Theory (IRT) that uses anchor items to calibrate new benchmarks, allowing for comparable scores even when models are evaluated on different datasets over time. The second paper presents a bilevel optimization approach that modifies model parameters during training to maximize the entropy of predictive distributions, directly targeting overconfidence and improving out-of-domain generalization. AI
IMPACT These methods could lead to more reliable and comparable LLM evaluations, improving the trustworthiness of benchmark results and aiding in the development of more robust models.
RANK_REASON Two academic papers published on arXiv proposing novel methods for LLM calibration.
- Bilevel Optimization for Cost Function Determination in Dynamic Simulation of Human Gait
- information entropy
- Large Language Models
- preference alignment
- question answering
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Eliya Habba
- generative question answering
- Gotit.pub
- Hugging Face
- Item Response Theory
- Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence
- out-of-domain generalization
- Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data.
- ScienceCast
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