Papers by Joseph Near
Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind (2026.findings-acl)
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| Challenge: | Existing persona datasets capture only trait, and ignore impact of state. |
| Approach: | They use a Reddit dataset to study user interactions with language models . they find that existing persona datasets capture only trait and ignore impact of state . |
| Outcome: | The proposed dataset decomposes variance and finds that LLMs are state-blind . the reward models react to user state, but inconsistently, the authors say . |
Differentially Private Learning Needs Better Model Initialization and Self-Distillation (2025.naacl-long)
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| Challenge: | Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. |
| Approach: | They propose a method that initializes a model using data synthesis from a small pre-trained LM with rigorous filtering, applies DP finetuning on private data, and performs self-distillation to refine outputs. |
| Outcome: | The proposed method outperforms vanilla DPSGD with significant improvements in lexical diversity and grammar errors. |