Papers by Do Long
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval (2024.acl-long)
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| Challenge: | Recent advances in large language models have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. |
| Approach: | They propose to use a multilingual multitask benchmark to evaluate large language models that can generate codes from natural language descriptions, repair buggy codes, and translate between languages. |
| Outcome: | The proposed model performs 7 tasks covering up to 11 languages with execution-level parallelism and 25 M document-level coding examples (16.5 B tokens) |
Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing enhancements of ExpertPrompting improve the large language model generation process. |
| Approach: | They propose a novel enhancement of ExpertPrompting to improve LLM generation by simulating multiple experts, aggregating their responses and selecting the best among individual and aggregated responses. |
| Outcome: | The proposed enhancement outperforms ExpertPrompting and comparable baselines in truthfulness, factuality, informativeness, usefulness and harmfulness. |
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)
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| Challenge: | Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors . |
| Approach: | They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation . |