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Bryan Li

5 accepted papers

2025

Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness

ACL 2025finding

The paradigm of retrieval-augmented generated (RAG) helps mitigate hallucinations of large language models (LLMs). However, RAG also introduces biases contained within the retrieved documents. These biases can be amplified in scenarios which are multilingual and culturally-sensitive, such as territo…

Cited by 0SourcePDFScholar
2024

Eliciting Better Multilingual Structured Reasoning from LLMs through Code

ACL 2024long

The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across si…

Cited by 8SourcePDFScholar
2024

This Land is Your, My Land: Evaluating Geopolitical Bias in Language Models through Territorial Disputes

NAACL 2024long

Do the Spratly Islands belong to China, the Philippines, or Vietnam? A pretrained large language model (LLM) may answer differently if asked in the languages of each claimant country: Chinese, Tagalog, or Vietnamese. This contrasts with a multilingual human, who would likely answer consistently. In…

2023

Bidirectional Language Models Are Also Few-shot Learners

ICLR 2023poster

Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitrary task can be reformulated as a natural language prompt, and a language model can be asked to generate the completion,…

Cited by 66SourcePDFScholar