4 papers
Reflective Context Learning: Studying the Optimization Primitives of Context Space
Nikita Vassilyev, William Berrios, Ruowang Zhang +3
Generally capable agents must learn from experience in ways that generalize across tasks and environments. The fundamental problems of learning, including credit assignment, overfi…
Goal Alignment in LLM-Based User Simulators for Conversational AI
Shuhaib Mehri, Xiaocheng Yang, Takyoung Kim +3
User simulators are essential to conversational AI, enabling scalable agent development and evaluation through simulated interactions. While current Large Language Models (LLMs) ha…
LMUnit: Fine-grained Evaluation with Natural Language Unit Tests
Jon Saad-Falcon, Rajan Vivek, William Berrios +6
As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge -- human evaluation is costly and noisy, while automated metrics…
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
Karel D'Oosterlinck, Winnie Xu, Chris Develder +5
Large Language Models (LLMs) are often aligned using contrastive alignment objectives and preference pair datasets. The interaction between model, paired data, and objective makes…