306 citations · 386 across the 87 of their papers we have counts for
18 papers · 1 filter
EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking
Minghan Li, Eric Gaussier, Juntao Li +1
Decoder-only LLM rerankers struggle with long documents: inference is costly and relevance signals can be diluted by irrelevant context. Motivated by an attention analysis indicati…
LOGO -- Long cOntext aliGnment via efficient preference Optimization
Zecheng Tang, Zechen Sun, Juntao Li +2
Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent…
Revealing and Mitigating the Local Pattern Shortcuts of Mamba
Wangjie You, Zecheng Tang, Juntao Li +2
Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-co…
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
Zecheng Tang, Keyan Zhou, Juntao Li +3
Long-context models (LCMs) have made remarkable strides in recent years, offering users great convenience for handling tasks that involve long context, such as document summarizati…
Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch
Yuyang Ding, Xinyu Shi, Xiaobo Liang +4
Improving the mathematical reasoning capabilities of Large Language Models (LLMs) is critical for advancing artificial intelligence. However, access to extensive, diverse, and high…
Beware of Calibration Data for Pruning Large Language Models
Yixin Ji, Yang Xiang, Juntao Li +5
As large language models (LLMs) are widely applied across various fields, model compression has become increasingly crucial for reducing costs and improving inference efficiency. P…
MemLong: Memory-Augmented Retrieval for Long Text Modeling
Weijie Liu, Zecheng Tang, Juntao Li +2
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs…
ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM
Zhaochen Su, Jun Zhang, Xiaoye Qu +6
Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has…
A Survey on Human Preference Learning for Large Language Models
Ruili Jiang, Kehai Chen, Xuefeng Bai +6
The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning, enhancin…
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Zhaochen Su, Juntao Li, Jun Zhang +6
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated ev…
Demonstration Augmentation for Zero-shot In-context Learning
Yi Su, Yunpeng Tai, Yixin Ji +3
Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations with…
OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure
Jikai Wang, Yi Su, Juntao Li +5
Autoregressive language models demonstrate excellent performance in various scenarios. However, the inference efficiency is limited by its one-step-one-word generation mode, which…
Timo: Towards Better Temporal Reasoning for Language Models
Zhaochen Su, Jun Zhang, Tong Zhu +4
Reasoning about time is essential for Large Language Models (LLMs) to understand the world. Previous works focus on solving specific tasks, primarily on time-sensitive question ans…
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and Bridging
Xiaobo Liang, Haoke Zhang, Helan hu +3
The rapid advancement of large language models has given rise to a plethora of applications across a myriad of real-world tasks, mainly centered on aligning with human intent. Howe…
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Dan Qiao, Yi Su, Pinzheng Wang +18
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment re…
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
Yixin Ji, Yang Xiang, Juntao Li +6
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating…
Rethinking Negative Instances for Generative Named Entity Recognition
Yuyang Ding, Juntao Li, Pinzheng Wang +3
Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen t…
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Zecheng Tang, Chenfei Wu, Zekai Zhang +8
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model…