1 citations · 1 across the 5 of their papers we have counts for
5 papers
TraceMem: Weaving Narrative Memory Schemata from User Conversational Traces
Yiming Shu, Pei Liu, Tiange Zhang +3
Sustaining long-term interactions remains a bottleneck for Large Language Models (LLMs), as their limited context windows struggle to manage dialogue histories that extend over tim…
MM-THEBench: Do Reasoning MLLMs Think Reasonably?
Zhidian Huang, Zijun Yao, Ji Qi +7
Recent advances in multimodal large language models (MLLMs) mark a shift from non-thinking models to post-trained reasoning models capable of solving complex problems through think…
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Haoyan Yang, Runxue Bao, Cao Xiao +4
LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing eff…
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +3
Large generative models such as large language models (LLMs) and diffusion models have revolutionized the fields of NLP and computer vision respectively. However, their slow infere…
Improving Factual Error Correction by Learning to Inject Factual Errors
Xingwei He, Qianru Zhang, A-Long Jin +3
Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviati…