106 citations · 130 across the 17 of their papers we have counts for
17 papers
MuSeR: Scalable Long-sequence Recommendation with Multi-interest Modeling
Yongkang Fu, Beining Bao, Yu Jiang +10
Ultra-long user behavior sequences carry rich signals of stable and diverse preferences, yet industrial recommender systems typically truncate histories to a few hundred actions un…
From 0-Order Selection to 2-Order Judgment: Combinatorial Hardening Exposes Compositional Failures in Frontier LLMs
Hanmeng Liu, Shichao Weng, Xiulai Liu +3
Multiple-choice reasoning benchmarks face dual challenges: rapid saturation from advancing models and data contamination that undermines static evaluations. Ad-hoc hardening method…
LASAR: Latent Adaptive Semantic Aligned Reasoning for Generative Recommendation
Yiwen Chen, Fuwei Zhang, Zehao Chen +7
Large Language Models (LLMs) have demonstrated powerful reasoning capabilities through Chain-of-Thought (CoT) in various tasks, yet the inefficiency of token-by-token generation hi…
ReEfBench: Quantifying the Reasoning Efficiency of LLMs
Zhizhang Fu, Yuancheng Gu, Chenkai Hu +2
Test-time scaling has enabled Large Language Models (LLMs) to tackle complex reasoning, yet the limitations of current Chain-of-Thought (CoT) evaluation obscures whether performanc…
GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation
Shichao Weng, Zhiqiang Wang, Yuhua Zhou +5
Geometric Problem Solving (GPS) poses a unique challenge for Multimodal Large Language Models (MLLMs), requiring not only the joint interpretation of text and diagrams but also ite…
Multimodal Fusion And Sparse Attention-based Alignment Model for Long Sequential Recommendation
Yongrui Fu, Jian Liu, Tao Li +3
Recent advances in multimodal recommendation enable richer item understanding, while modeling users' multi-scale interests across temporal horizons has attracted growing attention.…
Evaluating the Logical Reasoning Abilities of Large Reasoning Models
Hanmeng Liu, Yiran Ding, Zhizhang Fu +3
Large reasoning models, often post-trained on long chain-of-thought (long CoT) data with reinforcement learning, achieve state-of-the-art performance on mathematical, coding, and d…
Logical Reasoning in Large Language Models: A Survey
Hanmeng Liu, Zhizhang Fu, Mengru Ding +4
With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their abi…
Break the Chain: Large Language Models Can be Shortcut Reasoners
Mengru Ding, Hanmeng Liu, Zhizhang Fu +3
Recent advancements in Chain-of-Thought (CoT) reasoning utilize complex modules but are hampered by high token consumption, limited applicability, and challenges in reproducibility…
Logic Agent: Enhancing Validity with Logic Rule Invocation
Hanmeng Liu, Zhiyang Teng, Chaoli Zhang +1
Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks. Despite its advancements,…
GLoRE: Evaluating Logical Reasoning of Large Language Models
Hanmeng liu, Zhiyang Teng, Ruoxi Ning +4
Large language models (LLMs) have shown significant general language understanding abilities. However, there has been a scarcity of attempts to assess the logical reasoning capacit…
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
Hanmeng Liu, Ruoxi Ning, Zhiyang Teng +3
Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "ad…
LogiCoT: Logical Chain-of-Thought Instruction-Tuning
Hanmeng Liu, Zhiyang Teng, Leyang Cui +3
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enha…
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Linyi Yang, Shuibai Zhang, Libo Qin +6
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-t…
Solving Aspect Category Sentiment Analysis as a Text Generation Task
Jian Liu, Zhiyang Teng, Leyang Cui +2
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-s…
Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts
Hanmeng Liu, Leyang Cui, Jian Liu +1
Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts. Popular NLI datasets present the task at sentence-level. Wh…
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari +2
A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka t…