2 citations · 2 across the 14 of their papers we have counts for
15 papers
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation
Siyang Lyu, Xinghao Chen, Zhijing Sun +3
Chain-of-Thought (CoT) distillation transfers multi-step reasoning from large reasoning models to smaller students, but verbose teacher traces inflate both training and inference c…
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation
Shaomu Tan, Dawei Zhu, Ke Tran +5
Iterative self-refinement is a simple inference-time strategy for machine translation: an LLM revises its own translation over multiple inference-time passes. Yet document-scale re…
SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models
Yirong Sun, Yanjun Chen, Xin Qiu +8
Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness,…
A Survey on Latent Reasoning
Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng +30
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate s…
MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction
Xiao Wang, Jiahuan Pei, Diancheng Shui +4
Legal judgment prediction offers a compelling method to aid legal practitioners and researchers. However, the research question remains relatively under-explored: Should multiple d…
PLD: A Choice-Theoretic List-Wise Knowledge Distillation
Ejafa Bassam, Dawei Zhu, Kaigui Bian
Knowledge distillation is a model compression technique in which a compact "student" network is trained to replicate the predictive behavior of a larger "teacher" network. In logit…
Language models can learn implicit multi-hop reasoning, but only if they have lots of training data
Yuekun Yao, Yupei Du, Dawei Zhu +2
Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought. We investigate this capability using GP…
Same evaluation, more tokens: On the effect of input length for machine translation evaluation using Large Language Models
Tobias Domhan, Dawei Zhu
Accurately evaluating machine-translated text remains a long-standing challenge, particularly for long documents. Recent work has shown that large language models (LLMs) can serve…
The Accuracy Paradox in RLHF: When Better Reward Models Don't Yield Better Language Models
Yanjun Chen, Dawei Zhu, Yirong Sun +3
Reinforcement Learning from Human Feedback significantly enhances Natural Language Processing by aligning language models with human expectations. A critical factor in this alignme…
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models
Junyan Lin, Haoran Chen, Dawei Zhu +1
In recent years, multimodal large language models (MLLMs) have garnered significant attention from both industry and academia. However, there is still considerable debate on constr…
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…
From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks
Andreas Stephan, Dawei Zhu, Matthias Aßenmacher +2
To reduce the need for human annotations, large language models (LLMs) have been proposed as judges of the quality of other candidate models. The performance of LLM judges is typic…
Assessing "Implicit" Retrieval Robustness of Large Language Models
Xiaoyu Shen, Rexhina Blloshmi, Dawei Zhu +2
Retrieval-augmented generation has gained popularity as a framework to enhance large language models with external knowledge. However, its effectiveness hinges on the retrieval rob…
InternLM-Law: An Open Source Chinese Legal Large Language Model
Zhiwei Fei, Songyang Zhang, Xiaoyu Shen +9
While large language models (LLMs) have showcased impressive capabilities, they struggle with addressing legal queries due to the intricate complexities and specialized expertise r…