| Challenge: | Language models have shown impressive abilities in a range of natural language processing tasks. |
| Approach: | This tutorial will provide an overview of the latest advances in natural language processing . it will provide preliminaries of training foundation models on code and their common practices . |
| Outcome: | This tutorial aims to provide an overview of recent advances in code modeling . it provides preliminaries of training foundation models on code and their common practices . |
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How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code (2025.findings-emnlp)
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| Challenge: | Language models (LMs) have exhibited impressive abilities in generating code from natural language requirements. |
| Approach: | They propose to introduce various metrics with inter-code similarity to evaluate the diversity of generated code by comparing model-generated solutions with human-written ones. |
| Outcome: | The proposed method leverages LMs’ capabilities in code understanding and reasoning, resulting in a set of metrics that represent the number of algorithms in model-generated solutions. |
Modelling Natural Language, Programs, and their Intersection (N18-6)
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| Challenge: | a tutorial will explore the intersection of programming and natural language to make this goal a reality . |
| Approach: | This tutorial will focus on machine learning models of programs and natural language . it will discuss similarities and differences between programming and natural languages . |
| Outcome: | This tutorial will discuss the intersection of programming and natural language . it will cover automatic explanation of programs in natural language and automatic generation of programs from natural language specifications . |
Large Language Models Meet NL2Code: A Survey (2023.acl-long)
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Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou
| Challenge: | generating code from a natural language description is a pressing and significant challenge in code intelligence. |
| Approach: | They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks. |
| Outcome: | The proposed model is compared with existing models on the HumanEval benchmark. |
Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs (2025.emnlp-main)
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Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley
| Challenge: | Recent breakthrough models like OpenAI-o1 and DeepSeek-R1 show powerful task-solving capabilities, particularly advances in reasoning. |
| Approach: | They propose future research directions that may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
| Outcome: | The proposed research may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
| Approach: | This tutorial provides a broad and comprehensive introduction to pre-trained language models . it focuses on emerging methods that enable PLMs to perform diverse downstream tasks . |
| Outcome: | This tutorial focuses on the benefits of pre-trained language models and how to use them in NLP tasks. |
Deep Learning for Natural Language Inference (N19-5)
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
| Outcome: | This tutorial aims to answer questions about instruction-driven NLP . it focuses on the use of task instructions in a low-shot scenario . |
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized the field of natural language processing and artificial intelligence, creating new SOTAs and reaching human-level language understanding performance on a series of tasks and benchmarks. |
| Approach: | They propose to use an algorithm test set sourced from Introduction to Algorithm to assess LLMs' code execution abilities. |
| Outcome: | The proposed model can execute programs described in natural language as long as no heavy numeric computation is involved. |
Computational Expressivity of Neural Language Models (2024.acl-tutorials)
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| Challenge: | Language models (LMs) are at the forefront of NLP research due to their versatility across diverse tasks. |
| Approach: | This tutorial will provide a framework for formal analysis of modern language models using tools from formal language theory. |
| Outcome: | This tutorial will provide a framework for formal analysis of modern language models using tools from formal language theory (FLT). |
Cross-Task Generalization Abilities of Large Language Models (2024.naacl-srw)
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| Challenge: | a thesis proposal advocates for the crucial role of cross-task generalization in NLP systems. |
| Approach: | They propose to benchmark cross-task generalization abilities with diverse NLP tasks . they also propose to develop model architectures for improving cross- task generalization . |
| Outcome: | This paper compares cross-task generalization abilities with diverse NLP tasks . it also analyzes and predicts the generalization landscape of current state-of-the-art large language models . |