ACL 2024 Accepted Paper 浅浅分析

7月7日，晴。

ACL 2024的接受论文列表终于姗姗来迟，全网没有搜到相关解析，那我只能先吃个螃蟹了。

借助ChatGPT的辅助编程，我对于其论文和作者进行了一些浅浅的分析，主要从词云、主题类型、作者发表数量3个方面对于主会和findings的论文进行解读，其分析代码已开源。

主会论文词云

从上图可以看出，ACL 2024会议上，最显眼的就是“Large Language Model（大型语言模型）”，这说明大规模预训练模型依然是研究的核心。像“生成（Generation）”、“理解（Understanding）”、“推理（Reasoning）”和“评估（Evaluation）”这些关键词也频繁出现，表明研究者们致力于让这些模型变得更智能和可靠。跨语言和多模态研究也在升温，“多模态（Multimodal）”和“多语言（Multilingual）”的出现频率很高，显示出大家对提升模型处理多种输入形式和语言能力的兴趣。

此外，“任务（Task）”、“数据（Data）”和“基准（Benchmark）”这些词的高频使用，显示了对模型性能评估和数据集构建的重视，这些研究确保了模型在真实世界中的可靠性和有效性。交互和生成式AI应用的研究也很受关注，特别是“对话（Dialogue）”和“问答（Question Answering）”这些词汇，表明提升人机交互体验是一个重要方向。

最后，一些细化的研究方向如“零样本（Zero-Shot）”、“多跳（Multi-Hop）”和“对比学习（Contrastive Learning）”也在词云中占据了一席之地，显示了在细分任务和模型优化上的深入探索。

主会论文主题聚类

主题聚类如图所示，尽管用了t-sne，点仍然很散，这其实表明研究方向还是很多样的，具体而言这20个类别内容如下：
Cluster 0: multimodal, knowledge, learning, translation, nlp, detection, transformer, contrastive, semantic, language

Cluster 1: context, long, models, learning, language, large, aware, multi, data, demonstration

Cluster 2: models, language, large, reasoning, knowledge, evaluation, task, editing, experts, learning

Cluster 3: model, code, language, large, generation, learning, aware, uncertainty, multi, compiler

Cluster 4: llms, data, synthetic, style, text, prompts, low, quality, knowledge, jailbreak

Cluster 5: shot, zero, dialogue, stance, framework, resource, low, languages, detection, reranking

Cluster 6: question, answering, knowledge, multi, hop, domain, based, base, open, questions

Cluster 7: text, generation, image, evaluation, based, multi, generated, controllable, model, free

Cluster 8: natural, language, explanations, measuring, faithfulness, learning, inference, models, evaluating, said

Cluster 9: evaluating, capabilities, models, large, language, benchmark, multilingual, llms, capability, generation

Cluster 10: tuning, fine, parameter, efficient, models, language, instruction, large, rl, rank

Cluster 11: speech, translation, end, parsing, recognition, text, simultaneous, hate, foundation, semantic

Cluster 12: vision, representations, language, models, large, navigation, multilingual, multimodal, methods, hallucination

Cluster 13: document, event, extraction, level, relation, coreference, argument, multi, cross, learning

Cluster 14: multilingual, preference, reasoning, language, alignment, models, dataset, instruction, open, aya

Cluster 15: retrieval, augmented, generation, models, information, language, knowledge, multi, robustness, noise

Cluster 16: llm, agents, based, conversational, interactive, software, benchmarking, evaluation, mobile, attacks

Cluster 17: self, consistency, models, language, large, translation, reflection, distillation, learning, enhancing

Cluster 18: training, chinese, human, language, data, pre, preferences, models, model, benchmarking

Cluster 19: chain, thought, reasoning, prompting, multi, models, cot, language, modal, boosting

根据ACL 2024的聚类结果，我们可以看到当前自然语言处理（NLP）领域的几个主要研究趋势：

大型语言模型和多模态处理：研究集中在大型预训练模型的智能化和多模态数据的融合应用，强调跨语言和多模态学习的能力提升。
模型评估和优化：重点在于模型性能的评估、任务适配和参数优化，确保模型在实际应用中的可靠性和有效性。
生成与推理：对文本生成、代码生成以及复杂知识推理的深入探索，显示了对提高模型创造力和推理能力的重视。
人机交互和对话系统：加强了对话系统和问答系统的研究，特别是在提升交互体验和多任务处理能力方面。
安全性和数据质量：关注数据生成质量、模型安全性和资源有限环境下的处理方法，确保技术应用的可靠性和安全性。

主会论文中稿的高产论文作者

我对于一作以及最后一名作者的论文发表数量都进行了统计：

一作都是学术新星，最高的一作一人中了3篇主会，恭喜Zheng Chu, Yuanhe Tian以及Yilun Zhao。而最后作者里都是熟悉的老师。

findings的论文词云

词云上基本上和主会类似。

findings的论文主题聚类

20个类别分别是：

Cluster 0: llms, safety, data, abilities, capabilities, llm, iterative, multilingual, agent, investigating

Cluster 1: fine, efficient, tuning, parameter, grained, learning, models, language, editing, large

Cluster 2: event, detection, multimodal, dataset, extraction, enhancing, corpus, sql, linking, argument

Cluster 3: shot, zero, relation, extraction, entity, classification, learning, better, document, generate

Cluster 4: retrieval, multi, augmented, modal, generation, generative, information, llms, retriever, text

Cluster 5: tuning, instruction, based, sentiment, analysis, aspect, multi, data, task, transfer

Cluster 6: alignment, cross, lingual, preference, llm, contrastive, language, zero, shot, understanding

Cluster 7: evaluation, language, large, models, benchmark, chinese, based, grained, vision, fine

Cluster 8: translation, machine, text, generation, dataset, data, llm, summarization, neural, semantic

Cluster 9: question, answering, knowledge, visual, multi, reasoning, temporal, language, questions, retrieval

Cluster 10: pre, trained, models, language, training, universal, modal, large, chart, efficient

Cluster 11: models, language, large, evaluating, knowledge, text, training, benchmarking, instruction, generation

Cluster 12: model, language, large, editing, uncertainty, generation, aware, models, data, clinical

Cluster 13: modeling, memory, long, language, sequence, state, models, level, learning, guided

Cluster 14: reasoning, large, models, language, exploring, mathematical, chain, knowledge, thought, graphs

Cluster 15: context, learning, selection, example, compression, demonstrations, order, language, models, aware

Cluster 16: natural, augmentation, language, robustness, inference, data, domain, models, large, open

Cluster 17: decoding, graph, knowledge, speculative, structured, contrastive, rule, bayes, minimum, risk

Cluster 18: self, supervised, language, models, position, large, learning, speech, consistency, training

Cluster 19: end, speech, recognition, named, translation, entity, emotion, hate, dataset, implicit

findings论文高产作者：

一作分布：

最后作者的论文分布：

写在最后

以上只是进行了粗略的统计，在程序运行中难免有所疏漏，因此仅供参考。另外，作者统计时也有可能存在同名的情况重复统计，这里也并未作区分。

那么，我们1个月后，泰国曼谷见!