57 papers
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin
While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly…
GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs
Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin +1
Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at t…
Multi-Attribute Steering of Language Models via Targeted Intervention
Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin +1
Inference-time intervention (ITI) has emerged as a promising method for steering large language model (LLM) behavior in a particular direction (e.g., improving helpfulness) by inte…
Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation
Atin Pothiraj, Jaemin Cho, Yue Zhang +2
Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a la…
CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks
Ashwin Vinod, Ying Ding, Elias Stengel-Eskin
LLM agents in knowledge intensive question answering take retrieval and reasoning actions with incomplete knowledge about whether their current answer is uncertain, unsupported, or…
PragReST: Self-Reinforcing Counterfactual Reasoning for Pragmatic Language Understanding
Jihyung Park, Minchao Huang, Leqi Liu +1
Natural language understanding often depends on meanings that are implied rather than explicitly stated, requiring pragmatic reasoning. Despite strong performance on math and logic…