| 1 |
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models |
提出InstUPR以解决无监督段落重排序问题 |
large language model instruction following |
✅ |
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| 2 |
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition |
提出量化情感识别中大语言模型先验知识影响的方法 |
large language model |
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| 3 |
Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model |
提出基于规则的NLP系统与大语言模型比较以提取社会支持与孤立信息 |
large language model |
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| 4 |
KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for Fine-Tuning Korean Large Language Models |
提出KIT-19以提升韩语大语言模型的指令调优效果 |
large language model |
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| 5 |
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance |
提出数据混合法则以优化语言模型的数据混合比例 |
large language model |
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| 6 |
NSINA: A News Corpus for Sinhala |
提出NSINA以解决僧伽罗语自然语言处理数据匮乏问题 |
large language model |
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| 7 |
MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models |
提出MetaAligner以解决多目标对齐的通用性问题 |
large language model |
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| 8 |
LLMs Are Few-Shot In-Context Low-Resource Language Learners |
提出查询对齐方法以提升低资源语言的学习效果 |
large language model |
✅ |
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| 9 |
CodeS: Natural Language to Code Repository via Multi-Layer Sketch |
提出CodeS以解决自然语言到代码库生成问题 |
large language model |
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| 10 |
$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models |
提出LinkPrompt以解决Prompt模型的对抗攻击问题 |
large language model |
✅ |
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| 11 |
Linear Cross-document Event Coreference Resolution with X-AMR |
提出X-AMR以解决事件共指解析的高成本问题 |
large language model |
✅ |
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| 12 |
Enhanced Facet Generation with LLM Editing |
提出无搜索引擎的用户查询面向生成方法以提升信息检索效果 |
large language model |
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| 13 |
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis |
提出基于自然语言推理与概念嵌入的本体补全方法 |
large language model |
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| 14 |
New Intent Discovery with Attracting and Dispersing Prototype |
提出RAP框架以解决新意图发现问题 |
large language model |
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| 15 |
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback |
提出CoCoGen以解决项目特定上下文不足的问题 |
large language model |
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| 16 |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict |
提出RU22Fact以优化俄罗斯-乌克兰冲突的多语言可解释事实核查 |
large language model |
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| 17 |
Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units |
提出对话基础标注方法以提升对话系统的理解能力 |
large language model |
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| 18 |
TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques |
提出多领域机器生成文本检测技术以应对信息误导问题 |
large language model |
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| 19 |
Reasoning Runtime Behavior of a Program with LLM: How Far Are We? |
提出REval框架以评估代码LLM的推理能力 |
large language model |
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| 20 |
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases |
提出多阶段框架以解决信息提取中的定义偏差问题 |
large language model |
✅ |
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