18 citations · 28 across the 8 of their papers we have counts for
9 papers
Cache2LoRA: Understanding and Improving Context Internalization
Yi Fang, Gaurav Srivastava, Zhenyu Bi +2
Context-to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context. How reliably…
JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation
Zhenyu Bi, Gaurav Srivastava, Yang Li +4
While small language models (SLMs) have shown promise on various reasoning tasks, their ability to judge the correctness of answers remains unclear compared to large language model…
OPTAGENT: Optimizing Multi-Agent LLM Interactions Through Verbal Reinforcement Learning for Enhanced Reasoning
Zhenyu Bi, Meng Lu, Yang Li +4
Large Language Models (LLMs) have shown remarkable reasoning capabilities in mathematical and scientific tasks. To enhance complex reasoning, multi-agent systems have been proposed…
Bridging Literature and the Universe Via A Multi-Agent Large Language Model System
Xiaowen Zhang, Zhenyu Bi, Patrick Lachance +3
As cosmological simulations and their associated software become increasingly complex, physicists face the challenge of searching through vast amounts of literature and user manual…
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning
Gaurav Srivastava, Zhenyu Bi, Meng Lu +1
Large language models (LLMs) have improved significantly in their reasoning through extensive training on massive datasets. However, relying solely on additional data for improveme…
Population Aware Diffusion for Time Series Generation
Yang Li, Han Meng, Zhenyu Bi +2
Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data…
STOC-TOT: Stochastic Tree-of-Thought with Constrained Decoding for Complex Reasoning in Multi-Hop Question Answering
Zhenyu Bi, Daniel Hajialigol, Zhongkai Sun +2
Multi-hop question answering (MHQA) requires a model to retrieve and integrate information from multiple passages to answer a complex question. Recent systems leverage the power of…
AI for Biomedicine in the Era of Large Language Models
Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol +4
The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, pred…
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Xiaotao Gu, Zihan Wang, Zhenyu Bi +4
Identifying and understanding quality phrases from context is a fundamental task in text mining. The most challenging part of this task arguably lies in uncommon, emerging, and dom…