2 citations · 4 across the 6 of their papers we have counts for
8 papers
APEX: Speculate smarter, not deeper
Manvi Jha, Zach Zhang, Zhichao Xu +3
Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness depends on both the proposal…
From Self-Distillation to Self-Practice: Privileged Information for Multi-Turn Agents
Xingyu Su, Abhishek Kumar, Qing Ping +5
On-policy self-distillation (OPSD) has become a popular recipe for post-training LLM agents. It supervises the agent model at the token level with a stronger teacher view of the sa…
PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data
Zeyu He, Xuan Qi, Subramanian Chidambaram +4
Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a n…
An Empirical Study of Automating Agent Evaluation
Kang Zhou, Sangmin Woo, Haibo Ding +15
Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises:…
PILOT: Steering Synthetic Data Generation with Psychological & Linguistic Output Targeting
Caitlin Cisar, Emily Sheffield, Joshua Drake +5
Generative AI applications commonly leverage user personas as a steering mechanism for synthetic data generation, but reliance on natural language representations forces models to…
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation
Haris Riaz, Sourav Bhabesh, Vinayak Arannil +2
Recent smaller language models such Phi-3.5 and Phi-4 rely on synthetic data generated using larger Language models. Questions remain about leveraging synthetic data for other use…
ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformers
Vinayak Arannil, Tomal Deb, Atanu Roy
Early identification of Adverse Drug Events (ADE) is critical for taking prompt actions while introducing new drugs into the market. These ADEs information are available through va…
DoPAMine: Domain-specific Pre-training Adaptation from seed-guided data Mining
Vinayak Arannil, Neha Narwal, Sourav Sanjukta Bhabesh +5
Large Language Models (LLMs) have shown remarkable ability to generalize effectively across numerous industry domains while executing a range of tasks. Many of these competencies a…