Search Results for author: Wei-Lin Chen

Found 9 papers, 8 papers with code

InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales

1 code implementation19 Jun 2024 Zhepei Wei, Wei-Lin Chen, Yu Meng

Retrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs).

Denoising In-Context Learning +2

Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization

1 code implementation3 Jun 2024 Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, Wei-Lin Chen, Chao-Wei Huang, Yu Meng, Yun-Nung Chen

The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e. g., personalized search, LLM-as-a-judge).

Measuring Taiwanese Mandarin Language Understanding

2 code implementations29 Mar 2024 Po-Heng Chen, Sijia Cheng, Wei-Lin Chen, Yen-Ting Lin, Yun-Nung Chen

We present TMLU, a holistic evaluation suit tailored for assessing the advanced knowledge and reasoning capability in LLMs, under the context of Taiwanese Mandarin.

Large Language Models Perform Diagnostic Reasoning

1 code implementation18 Jul 2023 Cheng-Kuang Wu, Wei-Lin Chen, Hsin-Hsi Chen

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis.

Medical Diagnosis

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations

1 code implementation24 May 2023 Wei-Lin Chen, Cheng-Kuang Wu, Yun-Nung Chen, Hsin-Hsi Chen

Finally, we perform ICL for the test input with the pseudo-input-label pairs as demonstrations.

In-Context Learning

ZARA: Improving Few-Shot Self-Rationalization for Small Language Models

1 code implementation12 May 2023 Wei-Lin Chen, An-Zi Yen, Cheng-Kuang Wu, Hen-Hsen Huang, Hsin-Hsi Chen

Inspired by the implicit mental process of how human beings assess explanations, we present a novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to automatically construct pseudo-parallel data for self-training by reducing the problem of plausibility judgement to natural language inference.

Natural Language Inference

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