activity
20192024
most citedContrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

19 citations · 29 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CL2024

Learning to Retrieve Iteratively for In-Context Learning

Yunmo Chen, Tongfei Chen, Harsh Jhamtani +4

We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization. Finding an optimal portfolio of retrieved item…

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

MultiMUC: Multilingual Template Filling on MUC-4

William Gantt, Shabnam Behzad, Hannah YoungEun An +4

We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Ara…

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…

cs.CL2023

Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles

Weiting Tan, Haoran Xu, Lingfeng Shen +5

Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…