84 citations · 194 across the 103 of their papers we have counts for
19 papers · 2 filters
Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data
Jiapeng Wang, Yiwen Hu, Yanzipeng Gao +7
As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However,…
How Efficient Are Diffusion Language Models? A Critical Examination of Efficiency Evaluation Practices
Han Peng, Peiyu Liu, Zican Dong +5
Diffusion language models (DLMs) have emerged as a promising alternative to the long-dominant autoregressive (AR) paradigm, offering a parallelable decoding process that could yiel…
WUGNECTIVES: Novel Entity Inferences of Language Models from Discourse Connectives
Daniel Brubaker, William Sheffield, Junyi Jessy Li +1
The role of world knowledge has been particularly crucial to predict the discourse connective that marks the discourse relation between two arguments, with language models (LMs) be…
Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Xinyu Tang, Zhihao Lv, Xiaoxue Cheng +5
Large language models (LLMs) have shown impressive abilities in leveraging pretrained knowledge through prompting, but they often struggle with unseen tasks, particularly in data-s…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
Improving the Distributional Alignment of LLMs using Supervision
Gauri Kambhatla, Sanjana Gautam, Angela Zhang +4
The ability to accurately align LLMs with diverse population groups on subjective questions would have great value. In this work, we show that adding simple supervision can more co…
Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models
Muhammad Reza Qorib, Junyi Li, Hwee Tou Ng
Large language models (LLMs) have demonstrated impressive translation capabilities even without being explicitly trained on parallel data. This remarkable property has led some to…
Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs
William Sheffield, Kanishka Misra, Valentina Pyatkin +3
Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse…
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine
Sebastian Joseph, Lily Chen, Barry Wei +6
Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in adopting these systems for public h…
Strategic Dialogue Assessment: The Crooked Path to Innocence
Anshun Asher Zheng, Junyi Jessy Li, David I. Beaver
Language is often used strategically, particularly in high-stakes, adversarial settings, yet most work on pragmatics and LLMs centers on cooperativity. This leaves a gap in the sys…
ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework
Lisheng Huang, Yichen Liu, Jinhao Jiang +4
Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary…
Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models
Junyi Li, Hwee Tou Ng
Large language models (LLMs) have significantly advanced in reasoning tasks through reinforcement learning (RL) optimization, achieving impressive capabilities across various chall…
AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy
Sebastian Antony Joseph, Syed Murtaza Husain, Stella S. R. Offner +7
Large Language Models (LLMs) are being explored for applications in scientific research, including their capabilities to synthesize literature, answer research questions, generate…
EvalAgent: Discovering Implicit Evaluation Criteria from the Web
Manya Wadhwa, Zayne Sprague, Chaitanya Malaviya +3
Evaluation of language model outputs on structured writing tasks is typically conducted with a number of desirable criteria presented to human evaluators or large language models (…
QUDsim: Quantifying Discourse Similarities in LLM-Generated Text
Ramya Namuduri, Yating Wu, Anshun Asher Zheng +3
As large language models become increasingly capable at various writing tasks, their weakness at generating unique and creative content becomes a major liability. Although LLMs hav…
Behavioral Analysis of Information Salience in Large Language Models
Jan Trienes, Jörg Schlötterer, Junyi Jessy Li +1
Large Language Models (LLMs) excel at text summarization, a task that requires models to select content based on its importance. However, the exact notion of salience that LLMs hav…
Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?
Hye Sun Yun, Karen Y. C. Zhang, Ramez Kouzy +3
Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage researchers to present "positive" findings…
LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation
Zican Dong, Junyi Li, Jinhao Jiang +4
Large language models (LLMs) have gained extended context windows through scaling positional encodings and lightweight continual pre-training. However, this often leads to degraded…
SPRI: Aligning Large Language Models with Context-Situated Principles
Hongli Zhan, Muneeza Azmat, Raya Horesh +2
Aligning Large Language Models to integrate and reflect human values, especially for tasks that demand intricate human oversight, is arduous since it is resource-intensive and time…