Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large Language Models (LLMs) achieve remarkable success, but their static nature leads to inherent limitations and persistent mistakes in dynamic real-world scenarios. While Continual Instruction Tuning (CIT) and Continual Pre-training (CPT) are primary continual learning approaches, they struggle with scalable knowledge acquisition and maintaining model capabilities. To address these, we propose the Continue Evolving from Mistakes (CEM) method, a novel and data-efficient framework for continuous LLM evolution. Inspired by human learning, CEM establishes an iterative process: it efficiently collects targeted CPT data by robustly identifying LLM mistakes and uncertainties (via an Ambiguity-Aware Knowledge Collection (AAKC) algorithm), and employs a novel joint training paradigm that leverages CIT and CPT to assimilate knowledge efficiently while maintaining existing capabilities and mitigating catastrophic forgetting. Extensive experiments confirm CEM's effectiveness, yielding substantial accuracy gains for multiple models, increasing accuracy by up to 29.63%. Code and datasets are available on GitHub https://anonymous.4open.science/r/cem-BB25.

With the prevalence of Large Language Models (LLMs), recent studies have shifted paradigms and leveraged LLMs to tackle the challenging task of Text-to-SQL. Because of the complexity of real world databases, previous works adopt the retrieve-then-generate framework to retrieve relevant database schema and then to generate the SQL query. However, efficient embedding-based retriever suffers from lower retrieval accuracy, and more accurate LLM-based retriever is far more expensive to use, which hinders their applicability for broader applications. To overcome this issue, this paper proposes Gen-SQL, a novel generate-ground-regenerate framework, where we exploit prior knowledge from the LLM to enhance embedding-based retriever and reduce cost. Experiments on several datasets are conducted to demonstrate the effectiveness and scalability of our proposed method. We release our code and data at https://github.com/jieshi10/gensql.

The task of translating natural language questions into query languages has long been a central focus in semantic parsing.Recent advancements in Large Language Models (LLMs) have significantly accelerated progress in this field.However, existing studies typically focus on a single query language, resulting in methods with limited generalizability across different languages.In this paper, we formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages.We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework that explicitly diagnoses modelspecific weaknesses in handling these skeletons to synthesize targeted training data.Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-ofthe-art performance using only a small amount of synthesized data, highlighting the efficiency and generality of our approach and laying a solid foundation for unified research on Textto-Query tasks.We release our code

Text-to-SQL is the task of translating natural language questions into SQL queries based on relational databases.Different databases implement their own SQL dialects, leading to variations in syntax.As a result, SQL queries designed for one database may not execute properly in another, creating a dialect gap.Existing Text-to-SQL research primarily focuses on specific database systems, limiting adaptability to different dialects.This paper proposes a novel adaptive framework called Dialect-SQL, which employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap.Given a question, we guide Large Language Models (LLMs) to first generate ORM code, which is then parsed into SQL queries targeted for specific databases.However, there is a lack of high-quality Textto-Code datasets that enable LLMs to effectively generate ORM code.To address this issue, we propose a bootstrapping approach to synthesize ORM code, where verified ORM code is iteratively integrated into a demonstration pool that serves as in-context examples for ORM code generation.Our experiments demonstrate that Dialect-SQL significantly enhances dialect adaptability, outperforming traditional methods that generate SQL queries directly.

Abstract In a service system, operations engineers generally deploy numerous monitoring mechanisms in system components to detect anomalies caused by system faults and generate alerts, also known as alarms, that record the phenomenon of anomalies. Due to the topological relationships between system components, a system fault in a system component may affect other components and result in various local anomalies and generate multiple alerts across different components. Therefore, to facilitate troubleshooting, alerts of the same system fault are usually correlated into one group, called alert incident. However, although there are existing approaches that can automatically correlate alerts for operations engineers, analyzing alert incidents still rely on manual work. In this paper, we propose an approach, VOCE (Virtual On-Call Engineer). Using the emerging capabilities of a large language model, VOCE can automatically comprehend the anomaly information described by alerts and emulate the process of operations engineers analyzing an alert incident. Extensive experiments conducted on real alert incidents and two popular large language models demonstrate the effectiveness and efficiency of VOCE in automatically analyzing alert incidents.

System logs are critical for understanding the runtime behavior of information systems, yet their semi-structured nature poses challenges for effective utilization. Log parsing addresses this by transforming logs into structured representations, facilitating downstream tasks such as anomaly detection and failure analysis. Traditional approaches relying on statistical features or deep learning struggle with semantic understanding, efficiency, and adaptability to unseen data. Large language models (LLMs) offer new opportunities with their advanced semantic capabilities, yet current LLM-based log parsing methods are limited by sequential processing inefficiencies, suboptimal sampling strategies, and lack of robust template refinement mechanisms. To address these challenges, we propose EPAS (Efficient Parsing via Asynchronous Scheduling), a novel parser that utilizes asynchronous scheduling to optimize LLM- based log parsing. EPAS introduces three key innovations: a dynamic asynchronous mechanism to decouple LLM-based parsing from scheduling, maximizing efficiency; a controversy-based sampling mechanism to provide examples that help accurately parse challenging words; and an LLM-based validation task to semantically refine templates with minimal overhead. Experiments on benchmark datasets demonstrate that EPAS significantly outperforms state-of-the-art methods, achieving over a 360% improvement in parsing efficiency while maintaining high accuracy. In addition, EPAS achieves the best average performance across all four metrics, highlighting its effectiveness in both efficiency and accuracy.1

Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks. Nevertheless, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. Previous studies have devised sophisticated pipelines and prompts to induce large LMs to exhibit the capability for self-correction. However, large LMs are explicitly prompted to verify and modify their answers separately rather than completing all steps spontaneously like humans. Moreover, these complex prompts are extremely challenging for small LMs to follow. In this paper, we introduce the Intrinsic Self-Correction (ISC) in generative language models, aiming to correct the initial output of LMs in a self-triggered manner, even for those small LMs with 6 billion parameters. Specifically, we devise a pipeline for constructing self-correction data and propose Partial Answer Masking (PAM), aiming to endow the model with the capability for intrinsic self-correction through fine-tuning. We conduct experiments using LMs with parameters sizes ranging from 6 billion to 13 billion in two tasks, including commonsense reasoning and factual knowledge reasoning. Our experiments demonstrate that the outputs generated using ISC outperform those generated without self-correction. We believe that the output quality of even small LMs can be further improved by empowering them with the ability to intrinsic self-correct.

This paper presents ShellGPT, a pre-trained language model specifically designed to enhance the understanding of shell language which plays a crucial role in IT operations. Based on the GPT series of models, ShellGPT is trained on a corpus that aligns shell language with natural language, aiming to inject domain-specific knowledge into the model. The technique of pre-tokenization is employed to maximize the reuse of a general-purpose vocabulary, facilitating effective model transfer from general domain. Furthermore, a new pre-training objective, named equivalent command learning, is proposed to refine the command representations through modeling function equivalence of commands. To evaluate the performance of ShellGPT, we conduct fine-tuning on various downstream tasks related to shell language understanding. These tasks include command recommendation, command correction, and translation from natural language to shell command. Our experimental results demonstrate that ShellGPT outperforms other baseline models in terms of performance across almost all evaluated tasks. The findings from our experiments highlight the effectiveness of Shell-GPT in enhancing shell language understanding and demonstrate its potential for practical applications in IT operations.