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20212026
most citedContrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

19 citations · 28 across the 16 of their papers we have counts for

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7 papers · 1 filter

cs.CL2024

Upsample or Upweight? Balanced Training on Heavily Imbalanced Datasets

Tianjian Li, Haoran Xu, Weiting Tan +2

Data abundance across different domains exhibits a long-tailed distribution: few domains have abundant data, while most face data scarcity. Our work focuses on a multilingual setti…

cs.CL2024

SSR: Alignment-Aware Modality Connector for Speech Language Models

Weiting Tan, Hirofumi Inaguma, Ning Dong +2

Fusing speech into pre-trained language model (SpeechLM) usually suffers from inefficient encoding of long-form speech and catastrophic forgetting of pre-trained text modality. We…

cs.CL2024

It Takes Two: On the Seamlessness between Reward and Policy Model in RLHF

Taiming Lu, Lingfeng Shen, Xinyu Yang +3

Reinforcement Learning from Human Feedback (RLHF) involves training policy models (PMs) and reward models (RMs) to align language models with human preferences. Instead of focusing…

cs.CL2024

DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation

Weiting Tan, Jingyu Zhang, Lingfeng Shen +2

Non-autoregressive Transformers (NATs) are recently applied in direct speech-to-speech translation systems, which convert speech across different languages without intermediate tex…

cs.CL2024★ 2 cited

Streaming Sequence Transduction through Dynamic Compression

Weiting Tan, Yunmo Chen, Tongfei Chen +5

We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams. STAR dyn…

cs.CL2024★ 3 cited

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts

Lingfeng Shen, Weiting Tan, Sihao Chen +6

As the influence of large language models (LLMs) spans across global communities, their safety challenges in multilingual settings become paramount for alignment research. This pap…

cs.CL2024★ 19 cited

Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Haoran Xu, Amr Sharaf, Yunmo Chen +5

Moderate-sized large language models (LLMs) -- those with 7B or 13B parameters -- exhibit promising machine translation (MT) performance. However, even the top-performing 13B LLM-b…