某中心在NAACL 2024的30多篇论文技术概览
近年来，在十年前被深度学习革命化的自然语言处理和计算语言学领域，再次被大型语言模型（LLM）革命化。毫不意外，涉及LLM的研究——无论是将其作为研究对象还是作为其他自然语言处理应用的工具——在今年的计算语言学协会北美分会（NAACL）会议上占据主导地位。本指南将某中心的NAACL论文分为明确处理LLM的论文和未明确处理LLM的论文——尽管在许多情况下，后者提出的通用技术或数据集也可与LLM或更传统的模型结合使用。
LLM相关研究
智能体
- FLAP: 遵循流程的规划与约束解码 – Shamik Roy, Sailik Sengupta, Daniele Bonadiman, Saab Mansour, Arshit Gupta
属性值提取
- EIVEN: 使用多模态LLM进行高效的隐式属性值提取 – Henry Peng Zou, Gavin Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, Cornelia Caragea
持续学习
- Q-Tuning: 基于队列的提示微调用于终身少样本语言学习 – Yanhui Guo, Shaoyuan Xu, Jinmiao Fu, Jia (Kevin) Liu, Chaosheng Dong, Bryan Wang
对话系统
- 利用LLM进行对话质量测量 – Jinghan Jia, Abi Komma, Timothy Leffel, Xujun Peng, Ajay Nagesh, Tamer Soliman, Aram Galstyan, Anoop Kumar
幻觉缓解
- Less is more for improving automatic evaluation of factual consistency – Tong Wang, Ninad Kulkarni, Yanjun (Jane) Qi
- TofuEval: 评估LLM在主题聚焦对话摘要中的幻觉 – Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Justin Sun, Yi Zhang, Saab Mansour, Kathleen McKeown
- Towards improved multi-source attribution for long-form answer generation – Nilay Patel, Shivashankar Subramanian, Siddhant Garg, Pratyay Banerjee, Amita Misra
机器翻译
- A preference-driven paradigm for enhanced translation with large language models – Dawei Zhu, Sony Trenous, Xiaoyu Shen, Dietrich Klakow, Bill Byrne, Eva Hasler
自然语言处理
- Toward informal language processing: Knowledge of slang in large language models – Zhewei Sun, Qian Hu, Rahul Gupta, Richard Zemel, Yang Xu
问答系统
- Bring your own KG: Self-supervised program synthesis for zero-shot KGQA – Dhruv Agarwal, Rajarshi (Raj) Das, Sopan Khosla, Rashmi Gangadharaiah
推理
- CoMM: Collaborative multi-agent, multi-reasoning-path prompting for complex problem solving – Pei Chen, Boran Han, Shuai Zhang
推荐系统
- RecMind: Large language model powered agent for recommendation – Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Yingzhen Yang
基于人类反馈的强化学习
- RS-DPO: A hybrid rejection sampling and direct preference optimization method for alignment of large language models – Saeed Khaki, JinJin Li, Lan Ma, Liu Yang, Prathap Ramachandra
负责任AI
- ITERALIGN: Iterative constitutional alignment of large language models – Xiusi Chen, Hongzhi Wen, Sreyashi Nag, Chen Luo, Qingyu Yin, Ruirui Li, Zheng Li, Wei Wang
- MICo: Preventative detoxification of large language models through inhibition control – Roy Siegelmann, Ninareh Mehrabi, Palash Goyal, Prasoon Goyal, Lisa Bauer, Jwala Dhamala, Aram Galstyan, Rahul Gupta, Reza Ghanadan
- The steerability of large language models toward data-driven personas – Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
检索增强生成
- Enhancing contextual understanding in large language models through contrastive decoding – Zheng Zhao, Emilio Monti, Jens Lehmann, Haytham Assem
文本生成
- Low-cost generation and evaluation of dictionary example sentences – Bill Cai, Clarence Ng, Daniel Tan, Shelvia Hotama
- Multi-review fusion-in-context – Aviv Slobodkin, Ori Shapira, Ran Levy, Ido Dagan
视觉语言模型
- MAGID: An automated pipeline for generating synthetic multi-modal datasets – Hossein Aboutalebi, Justin Sun, Hwanjun Song, Yusheng Xie, Arshit Gupta, Hang Su, Igor Shalyminov, Nikolaos Pappas, Siffi Singh, Saab Mansour
- Prompting vision-language models for aspect-controlled generation of referring expressions – Danfeng Guo, Sanchit Agarwal, Arpit Gupta, Jiun-Yu Kao, Emre Barut, Tagyoung Chung, Jing Huang, Mohit Bansal
通用与经典技术
对话智能体
- Leveraging interesting facts to enhance user engagement with conversational interfaces – Nikhita Vedula, Giuseppe Castellucci, Eugene Agichtein, Oleg Rokhlenko, Shervin Malmasi
信息提取
- Leveraging customer feedback for multi-modal insight extraction – Sandeep Sricharan Mukku, Abinesh Kanagarajan, Pushpendu Ghosh, Chetan Aggarwal
- REXEL: An end-to-end model for document-level relation extraction and entity linking – Nacime Bouziani, Shubhi Tyagi, Joseph Fisher, Jens Lehmann, Andrea Pierleoni
机器学习
- DEED: Dynamic early exit on decoder for accelerating encoder-decoder transformer models – Peng Tang, Pengkai Zhu, Tian Li, Srikar Appalaraju, Vijay Mahadevan, R. Manmatha
机器翻译
- How lexical is bilingual lexicon induction? – Harsh Kohli, Helian Feng, Nicholas Dronen, Calvin McCarter, Sina Moeini, Ali Kebarighotbi
- M3T: A new benchmark dataset for multi-modal document-level machine translation – Benjamin Hsu, Xiaoyu Liu, Huayang Li, Yoshinari Fujinuma, Maria Nădejde, Xing Niu, Yair Kittenplon, Ron Litman, Raghavendra Pappagari
负责任AI
- Mitigating bias for question answering models by tracking bias influence – Mingyu Derek Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung, Wei Wang, Kai-Wei Chang, Nanyun Peng
语义检索
- Extremely efficient online query encoding for dense retrieval – Nachshon Cohen, Yaron Fairstein, Guy Kushilevitz
文本摘要
- CCSUM: A large-scale and high-quality dataset for abstractive news summarization – Xiang Jiang, Markus Dreyer
- Semi-supervised dialogue abstractive summarization via high-quality pseudolabel selection – Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov, Hwanjun Song, Saab Mansour
视觉问答
- Multiple-question multiple-answer text-VQA – Peng Tang, Srikar Appalaraju, R. Manmatha, Yusheng Xie, Vijay Mahadevan
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