116 citations · 228 across the 20 of their papers we have counts for
20 papers
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
Yun He, Wenzhe Li, Hejia Zhang +22
Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, a…
Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning
Melanie Sclar, Jane Yu, Maryam Fazel-Zarandi +4
Do large language models (LLMs) have theory of mind? A plethora of papers and benchmarks have been introduced to evaluate if current models have been able to develop this key abili…
Self-Consistency Preference Optimization
Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang +6
Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area. However, existing techniques often fail to improve complex…
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning
Da JU, Song Jiang, Andrew Cohen +8
Travel planning is a challenging and time-consuming task that aims to find an itinerary which satisfies multiple, interdependent constraints regarding flights, accommodations, attr…
Self-Taught Evaluators
Tianlu Wang, Ilia Kulikov, Olga Golovneva +7
Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the s…
PathFinder: Guided Search over Multi-Step Reasoning Paths
Olga Golovneva, Sean O'Brien, Ramakanth Pasunuru +4
With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However…