3 citations · 4 across the 10 of their papers we have counts for
10 papers
From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models
Junlong Tong, Zilong Wang, YuJie Ren +4
Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To…
AutoDataset: A Lightweight System for Continuous Dataset Discovery and Search
Junzhe Yang, Xinghao Chen, Yunuo Liu +3
The continuous expansion of task-specific datasets has become a major driver of progress in machine learning. However, discovering newly released datasets remains difficult, as exi…
Think-as-You-See: Streaming Chain-of-Thought Reasoning for Large Vision-Language Models
Jialiang Zhang, Junlong Tong, Junyan Lin +4
Large Vision Language Models (LVLMs) exhibit strong Chain-of-Thought (CoT) capabilities, yet most existing paradigms assume full-video availability before inference, a batch-style…
The Trace Is the State: Exact Credit Assignment for LLM Agent Teams
Yanjun Chen, Yirong Sun, Hanlin Wang +5
Credit assignment for a team of LLM agents, what each message was worth, has mostly been treated as prediction: a learned critic, a trajectory-level score, or an agent ablation sta…
Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning
Xinghao Chen, Zhijing Sun, Wenjin Guo +8
Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought (CoT) prompting. However, CoT prompting greatly increases computational demands, which has prompted g…
Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation
Yirong Sun, Dawei Zhu, Yanjun Chen +3
Large language models (LLMs) have excelled in various NLP tasks, including machine translation (MT), yet most studies focus on sentence-level translation. This work investigates th…
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?
Dawei Zhu, Pinzhen Chen, Miaoran Zhang +3
Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.…
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Dawei Zhu, Sony Trenous, Xiaoyu Shen +3
Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of paralle…
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis
Miaoran Zhang, Vagrant Gautam, Mingyang Wang +4
In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although…
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects
David Ifeoluwa Adelani, Hannah Liu, Xiaoyu Shen +5
Despite the progress we have recorded in the last few years in multilingual natural language processing, evaluation is typically limited to a small set of languages with available…