2 citations · 4 across the 14 of their papers we have counts for
8 papers · 1 filter
Query as Anchor: Scenario-Adaptive User Representation via Large Language Model
Jiahao Yuan, Yike Xu, Jinyong Wen +9
Industrial-scale user representation learning requires balancing robust universality with acute task-sensitivity. However, existing paradigms primarily yield static, task-agnostic…
GISA: A Benchmark for General Information-Seeking Assistant
Yutao Zhu, Xingshuo Zhang, Maosen Zhang +9
The advancement of large language models (LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web…
How Do Decoder-Only LLMs Perceive Users? Rethinking Attention Masking for User Representation Learning
Jiahao Yuan, Yike Xu, Jinyong Wen +8
Decoder-only large language models are increasingly used as behavioral encoders for user representation learning, yet the impact of attention masking on the quality of user embeddi…
MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity
Kaiyuan Zhang, Chenghao Yang, Zhoufutu Wen +19
As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behav…
Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
Ang Lv, Yuhan Chen, Kaiyi Zhang +5
In this paper, we delve into several mechanisms employed by Transformer-based language models (LLMs) for factual recall tasks. We outline a pipeline consisting of three major steps…
StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses
Jia-Nan Li, Quan Tu, Cunli Mao +3
Standard Large Language Models (LLMs) struggle with handling dialogues with long contexts due to efficiency and consistency issues. According to our observation, dialogue contexts…
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models
Changyu Chen, Xiting Wang, Ting-En Lin +6
In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains. Earlier fine-tuning approach…
An Analysis and Mitigation of the Reversal Curse
Ang Lv, Kaiyi Zhang, Shufang Xie +4
Recent research observed a noteworthy phenomenon in large language models (LLMs), referred to as the ``reversal curse.'' The reversal curse is that when dealing with two entities,…