84 citations · 194 across the 103 of their papers we have counts for
31 papers · 1 filter
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…
PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents
Zikang Liu, Junyi Li, Wayne Xin Zhao +3
Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) promise human-like interaction with software applications, yet long-horizon tasks remain c…
AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Minwei Kong, Ao Qu, Xiaotong Guo +12
Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…
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…
LLMs Lean on Priors, Not Programming Language Semantics
Aditya Thimmaiah, Jiyang Zhang, Jayanth Srinivasa +2
Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pretraining. Program execution provides…
Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design
Yihong Liu, Junyi Li, Wayne Xin Zhao +2
Combinatorial optimization problems are traditionally tackled with handcrafted heuristic algorithms, which demand extensive domain expertise and significant implementation effort.…
Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search
Xin Lai, Junyi Li, Wei Li +3
Recent advances in large multimodal models have leveraged image-based tools with reinforcement learning to tackle visual problems. However, existing open-source approaches often ex…
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang, Junyi Li, Xin Lai +3
Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. How…
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…
A Tool for Generating Exceptional Behavior Tests With Large Language Models
Linghan Zhong, Samuel Yuan, Jiyang Zhang +4
Exceptional behavior tests (EBTs) are crucial in software development for verifying that code correctly handles unwanted events and throws appropriate exceptions. However, prior re…
TokBench: Evaluating Your Visual Tokenizer before Visual Generation
Junfeng Wu, Dongliang Luo, Weizhi Zhao +6
In this work, we reveal the limitations of visual tokenizers and VAEs in preserving fine-grained features, and propose a benchmark to evaluate reconstruction performance for two ch…
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…
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
Xiaoxue Cheng, Junyi Li, Zhenduo Zhang +4
Large reasoning models (LRMs) have demonstrated strong performance on complex reasoning tasks, but often suffer from overthinking, generating redundant content regardless of task d…
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…
Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Xiaolei Wang, Chunxuan Xia, Junyi Li +5
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies i…
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 (…