Yejin Choi · page 4
Papers
- Biased LLMs can Influence Political Decision-Making (2025 · 20 citations)
The paper asks whether partisan bias in chat-based LLMs can change people's political views and choices during short, free-form conversations. - StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements (2024 · 16 citations)
Authorship obfuscation rewrites a text so that its author is hard to identify, while keeping the content and fluency. - When One LLM Drools, Multi-LLM Collaboration Rules (2026 · 14 citations)
This position paper by Feng et al. (ACL 2026) argues that a single general-purpose LLM cannot reliably handle complex, context-dependent, or subjective settings. It contributes no new dataset or model. Instead, it makes a conceptual case that any one model leaves gaps on three fronts. It misses parts of real-world… - Tailoring Self-Rationalizers with Multi-Reward Distillation (2023 · 14 citations)
Free-text rationales from language models are usually judged only by whether they raise answer accuracy, and good self-rationalization has been thought to require very large models such as 175B GPT-3. - Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning (2026 · 9 citations)
Reinforcement learning (RL) has improved LLM reasoning mainly in math and code, where answers can be checked automatically. - Towards Execution-Grounded Automated AI Research (2026 · 7 citations)
LLMs can propose research ideas that sound convincing but fail when tested. This paper asks two questions: can idea testing be automated at scale, and can models improve from the measured results? The authors built an automated executor. An Implementer turns natural-language ideas into code diffs, a Scheduler assigns… - Detecting English Writing Styles For Non Native Speakers (2017 · 1 citation)
The authors try to guess an English writer's native language from everyday, informal text at a much larger scale than earlier learner-corpus studies such as ICLE.
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