2 citations · 2 across the 8 of their papers we have counts for
13 papers · 1 filter
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +241
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…
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…
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…