14 citations · 34 across the 31 of their papers we have counts for
46 papers · 1 filter
Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL
Skylar Zhai, Jingcheng Liang, Dongyeop Kang
Reinforcement fine-tuning improves the reasoning ability of large language models, but it can also encourage them to answer unanswerable queries by guessing or hallucinating missin…
Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence in Narratives?
Karin de Langis, Püren Öncel, Ryan Peters +4
Leveraging a dataset of paired narratives, we investigate the extent to which large language models (LLMs) can reliably separate incoherent and coherent stories. A probing study fi…
Tracing How Annotators Think: Augmenting Preference Judgments with Reading Processes
Karin de Langis, William Walker, Khanh Chi Le +1
We propose an annotation approach that captures not only labels but also the reading process underlying annotators' decisions, e.g., what parts of the text they focus on, re-read o…
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs
Karin de Langis, Jong Inn Park, Andreas Schramm +5
Large language models (LLMs) exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these behaviors reflect human-like cognition versus advanced patter…
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
Zae Myung Kim, Chanwoo Park, Vipul Raheja +2
Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reli…
Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation
Jong Inn Park, Maanas Taneja, Qianwen Wang +1
Generating engaging, accurate short-form videos from scientific papers is challenging due to content complexity and the gap between expert authors and readers. Existing end-to-end…