20 citations · 46 across the 42 of their papers we have counts for
21 papers · 1 filter
SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness
Tanmay Parekh, Jeffrey Kwan, Jiarui Yu +6
Social media is often the first place where communities discuss the latest societal trends. Prior works have utilized this platform to extract epidemic-related information (e.g. in…
Control Large Language Models via Divide and Conquer
Bingxuan Li, Yiwei Wang, Tao Meng +2
This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG). We systematically ev…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization
Zhecheng Li, Yiwei Wang, Bryan Hooi +4
Cross-lingual summarization (CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at…
Vulnerability of LLMs to Vertically Aligned Text Manipulations
Zhecheng Li, Yiwei Wang, Bryan Hooi +4
Vertical text input is commonly encountered in various real-world applications, such as mathematical computations and word-based Sudoku puzzles. While current large language models…
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
Yuankai Li, Jia-Chen Gu, Di Wu +2
Retrieval-augmented generation (RAG) can supplement large language models (LLMs) by integrating external knowledge. However, as the number of retrieved documents increases, the inp…
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Di Wu, Hongwei Wang, Wenhao Yu +3
Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized re…
MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
Wenbo Hu, Jia-Chen Gu, Zi-Yi Dou +4
Existing multimodal retrieval benchmarks primarily focus on evaluating whether models can retrieve and utilize external textual knowledge for question answering. However, there are…
Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding
Cheng Wang, Yiwei Wang, Bryan Hooi +3
The training data in large language models is key to their success, but it also presents privacy and security risks, as it may contain sensitive information. Detecting pre-training…
QUDSELECT: Selective Decoding for Questions Under Discussion Parsing
Ashima Suvarna, Xiao Liu, Tanmay Parekh +2
Question Under Discussion (QUD) is a discourse framework that uses implicit questions to reveal discourse relationships between sentences. In QUD parsing, each sentence is viewed a…
Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation
Di Wu, Jia-Chen Gu, Fan Yin +2
Retrieval-augmented language models (RALMs) have shown strong performance and wide applicability in knowledge-intensive tasks. However, there are significant trustworthiness concer…
Event Detection from Social Media for Epidemic Prediction
Tanmay Parekh, Anh Mac, Jiarui Yu +8
Social media is an easy-to-access platform providing timely updates about societal trends and events. Discussions regarding epidemic-related events such as infections, symptoms, an…
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2
Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…
Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
Hritik Bansal, Ashima Suvarna, Gantavya Bhatt +3
A common technique for aligning large language models (LLMs) relies on acquiring human preferences by comparing multiple generations conditioned on a fixed context. This method, ho…
DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation
Xueqing Wu, Rui Zheng, Jingzhen Sha +6
Data analysis is a crucial analytical process to generate in-depth studies and conclusive insights to comprehensively answer a given user query for tabular data. In this work, we a…
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
Fan Yin, Jayanth Srinivasa, Kai-Wei Chang
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs), which serves as a crucial step in building trust between humans and…
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation
Di Wu, Wasi Uddin Ahmad, Kai-Wei Chang
This study addresses the application of encoder-only Pre-trained Language Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of domain-tailored encoder-onl…
On Prompt-Driven Safeguarding for Large Language Models
Chujie Zheng, Fan Yin, Hao Zhou +5
Prepending model inputs with safety prompts is a common practice for safeguarding large language models (LLMs) against queries with harmful intents. However, the underlying working…
ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models
Rohan Wadhawan, Hritik Bansal, Kai-Wei Chang +1
Many real-world tasks require an agent to reason jointly over text and visual objects, (e.g., navigating in public spaces), which we refer to as context-sensitive text-rich visual…
DeepEdit: Knowledge Editing as Decoding with Constraints
Yiwei Wang, Muhao Chen, Nanyun Peng +1
How to edit the knowledge in multi-step reasoning has become the major challenge in the knowledge editing (KE) of large language models (LLMs). The difficulty arises because the ha…
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue
Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma +4
Model editing is a technique that edits the large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining. While current mod…