20 citations · 54 across the 50 of their papers we have counts for
39 papers · 1 filter
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards
Wenbo Hu, Shraman Pramanick, Krishna Kumar Singh +3
Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated…
On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training
Xueqing Wu, Yu-Chi Lin, Kai-Wei Chang +1
Post-training has greatly improved reasoning in frontier vision-language models, yet its gains for perception remain comparatively limited, creating a bottleneck for end-to-end vis…
Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models
Pei-Fu Guo, Ya-An Tsai, Chun-Chia Hsu +6
While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, many real-world tasks require unders…
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs
Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu +6
Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior ex…
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning
Jia-Chen Gu, Junyi Zhang, Di Wu +3
As retrieval-augmented generation (RAG) tackles complex tasks, increasingly expanded contexts offer richer information, but at the cost of higher latency and increased cognitive lo…
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning
Tanmay Parekh, Kartik Mehta, Ninareh Mehrabi +2
Zero-shot Event Detection (ED), the task of identifying event mentions in natural language text without any training data, is critical for document understanding in specialized dom…
Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization
Di Wu, Jia-Chen Gu, Kai-Wei Chang +1
Selective retrieval aims to make retrieval-augmented generation (RAG) more efficient and reliable by skipping retrieval when an LLM's parametric knowledge suffices. Despite promisi…
Enhancing LLM Character-Level Manipulation via Divide and Conquer
Zhen Xiong, Yujun Cai, Bryan Hooi +3
Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks. However, they exhibit notable weak…
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
Yunzhi Yao, Jizhan Fang, Jia-Chen Gu +4
Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs). While existing KE methods can update isolated facts, they ofte…
Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments
Jiaxi Li, Yiwei Wang, Kai Zhang +5
Large language models (LLMs) have been widely adopted in various downstream task domains. However, their abilities to directly recall and apply factual medical knowledge remains un…
DRS: Deep Question Reformulation With Structured Output
Zhecheng Li, Yiwei Wang, Bryan Hooi +3
Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions t…
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…
Detecting Machine-Generated Long-Form Content with Latent-Space Variables
Yufei Tian, Zeyu Pan, Nanyun Peng
The increasing capability of large language models (LLMs) to generate fluent long-form texts is presenting new challenges in distinguishing machine-generated outputs from human-wri…
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…
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…
REFFLY: Melody-Constrained Lyrics Editing Model
Songyan Zhao, Bingxuan Li, Yufei Tian +1
Automatic melody-to-lyric (M2L) generation aims to create lyrics that align with a given melody. While most previous approaches generate lyrics from scratch, revision, editing plai…
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…
Evaluating Human Alignment and Model Faithfulness of LLM Rationale
Mohsen Fayyaz, Fan Yin, Jiao Sun +1
We study how well large language models (LLMs) explain their generations through rationales -- a set of tokens extracted from the input text that reflect the decision-making proces…
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…
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs
Xueqing Wu, Zongyu Lin, Songyan Zhao +4
Visual programs are executable code generated by large language models to address visual reasoning problems. They decompose complex questions into multiple reasoning steps and invo…
Re-ReST: Reflection-Reinforced Self-Training for Language Agents
Zi-Yi Dou, Cheng-Fu Yang, Xueqing Wu +2
Finetuning language agents with reasoning-action trajectories is effective, but obtaining these trajectories from human annotations or stronger models is costly and sometimes impra…