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Xuan Long Do

7 accepted papers

2024

Prompt Optimization via Adversarial In-Context Learning

ACL 2024long

We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompts for in-context learning (ICL). Inspired by adversarial learning, adv-ICL is implemented as a two-player game between a generator and discriminator, with LLMs acting as both. In each round, given an input prefixed…

2024

ToXCL: A Unified Framework for Toxic Speech Detection and Explanation

NAACL 2024long

The proliferation of online toxic speech is a pertinent problem posing threats to demographic groups. While explicit toxic speech contains offensive lexical signals, implicit one consists of coded or indirect language. Therefore, it is crucial for models not only to detect implicit toxic speech but…

2024

XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval

ACL 2024long

Recently, pre-trained large language models (LLMs) have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been perform…

Cited by 11SourcePDFScholar
2023

Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation

ACL 2023long

Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expe…

2022

ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

ACL 2022findings

Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus…

2022

CoHS-CQG: Context and History Selection for Conversational Question Generation

COLING 2022main

Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditional single-turn question generation (SQG), CQG is more challenging in the sense that the generated question is required…

2022

OpenCQA: Open-ended Question Answering with Charts

EMNLP 2022main

Charts are very popular to analyze data and convey important insights. People often analyze visualizations to answer open-ended questions that require explanatory answers. Answering such questions are often difficult and time-consuming as it requires a lot of cognitive and perceptual efforts. To add…