20 citations · 84 across the 24 of their papers we have counts for
26 papers
Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
Xi Li, Shu Zhao, Xiaohan Zou +6
Multi-modal large language models (MLLMs) integrate heterogeneous modalities through modality alignment and fusion, enabling stronger understanding and reasoning. However, this arc…
Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
Jihyun Janice Ahn, Ryo Kamoi, Berk Atil +34
LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify…
NeuroGen: Neural Network Parameter Generation via Large Language Models
Jiaqi Wang, Yusen Zhang, Xi Li
Acquiring the parameters of neural networks (NNs) has been one of the most important problems in machine learning since the inception of NNs. Traditional approaches, such as backpr…
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
Yusen Zhang, Wenliang Zheng, Aashrith Madasu +14
High-resolution image (HRI) understanding aims to process images with a large number of pixels, such as pathological images and agricultural aerial images, both of which can exceed…
GREATERPROMPT: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization
Wenliang Zheng, Sarkar Snigdha Sarathi Das, Yusen Zhang +1
LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significan…
Verbosity Veracity: Demystify Verbosity Compensation Behavior of Large Language Models
Yusen Zhang, Sarkar Snigdha Sarathi Das, Rui Zhang
Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as halluc…
GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang +3
The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range…
VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information
Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das +2
Large Vision Language Models (LVLMs) have achieved remarkable performance in various vision-language tasks. However, it is still unclear how accurately LVLMs can perceive visual in…
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang +15
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, a…
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Yusen Zhang, Ruoxi Sun, Yanfei Chen +3
Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs). Two common strategies have emerged: 1) reducing the i…
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
Xi Li, Ruofan Mao, Yusen Zhang +3
Large Language Models (LLMs), especially those accessed via APIs, have demonstrated impressive capabilities across various domains. However, users without technical expertise often…
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Ryo Kamoi, Yusen Zhang, Nan Zhang +2
Self-correction is an approach to improving responses from large language models (LLMs) by refining the responses using LLMs during inference. Prior work has proposed various self-…
An Improved Reversible Data Hiding Algorithm Based on Reconstructed Mapping for PVO-k
Yusen Zhang, Haoyun Xu, Jingwen Li
Reversible Data Hiding (RDH) is a practical and efficient technique for information encryption. Among its methods, the Pixel-Value Ordering (PVO) algorithm and its variants primari…
Evaluating LLMs at Detecting Errors in LLM Responses
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou +12
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted o…
A General Benchmark Framework is Dynamic Graph Neural Network Need
Yusen Zhang
Dynamic graph learning is crucial for modeling real-world systems with evolving relationships and temporal dynamics. However, the lack of a unified benchmark framework in current r…
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
Nan Zhang, Yusen Zhang, Wu Guo +2
Summaries of medical text shall be faithful by being consistent and factual with source inputs, which is an important but understudied topic for safety and efficiency in healthcare…
Fair Abstractive Summarization of Diverse Perspectives
Yusen Zhang, Nan Zhang, Yixin Liu +9
People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and pol…
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
Yusen Zhang, Jun Wang, Zhiguo Wang +1
Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms…
MACSum: Controllable Summarization with Mixed Attributes
Yusen Zhang, Yang Liu, Ziyi Yang +6
Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, exis…
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
Yusen Zhang, Ansong Ni, Ziming Mao +6
Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unab…
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Ziming Mao, Chen Henry Wu, Ansong Ni +7
Transformer-based models have achieved state-of-the-art performance on short-input summarization. However, they still struggle with summarizing longer text. In this paper, we prese…
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Yusen Zhang, Ansong Ni, Tao Yu +6
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…
SummerTime: Text Summarization Toolkit for Non-experts
Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma +5
Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summ…
Logic-Consistency Text Generation from Semantic Parses
Chang Shu, Yusen Zhang, Xiangyu Dong +3
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries. This is challenging due to two reason…