Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose **EXPO-SQL** (**EX**ecution-based clause-level **P**olicy **O**ptimization for Text-to-**SQL**) which provides fine-grained supervision through clause-level rewards. To assign clause-level rewards, our method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution. Experiments on widely-used Text-to-SQL benchmarks demonstrate that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and RL-based methods through fine-grained clause-level learning. Our code is available at https://github.com/jhn25/EXPO-SQL.

Recently, Large Language Models (LLM) have emerged as a promising paradigm for sequential recommendation. In sequential recommendation, effectively integrating diverse user preferences is essential for improving LLM performance, as users often exhibit multiple interests across different contexts. However, most existing LLM-based methods rely primarily on item descriptions or utilize user preferences independently. As a result, they overlook the relationships among preferences and fail to filter out less-relevant items that introduce noise. This makes it difficult to accurately capture the user’s interests, leading to suboptimal recommendations. To overcome these limitations, we propose UCGRec (User-Centric Graph Learning for LLM-based Sequential Recommendation), a novel method that effectively integrates diverse user-relevant preference signals into a unified user-centric graph. Then, we inject the graph-based knowledge into the LLM through end-to-end training with graph neural networks. We conduct extensive experiments on four widely used sequential real-world recommendation datasets. Our experimental results demonstrate that UCGRec significantly outperforms conventional and state-of-the-art LLM-based methods.

As Large Language Models (LLMs) have become capable of generating long and descriptive code summaries, accurate and reliable evaluation of factual consistency has become a critical challenge. However, previous evaluation methods are primarily designed for short summaries of isolated code snippets. Consequently, they struggle to provide fine-grained evaluation of multi-sentence functionalities and fail to accurately assess dependency context commonly found in real-world code summaries.To address this, we propose ReFEree, a reference-free and fine-grained method for evaluating factual consistency in real-world code summaries. We define factual inconsistency criteria specific to code summaries and evaluate them at the segment level using these criteria along with dependency information. These segment-level results are then aggregated into a fine-grained score. We construct a code summarization benchmark with human-annotated factual consistency labels. The evaluation results demonstrate that ReFEree achieves the highest correlation with human judgment among 13 baselines, improving 15-18% over the previous state-of-the-art. Our code and data are available at https://github.com/bsy99615/ReFEree.git.

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model’s output information. They are not applicable in real-world scenarios, as in hard black-box settings where the target model is closed and inaccessible. Even the recently proposed hard black-box attacks still require many queries and demand extremely high costs for training adversarial generators. To address these challenges, we propose Q-faker (Query-free Hard Black-box Attacker), a novel and efficient method that generates adversarial examples without accessing the target model. To avoid accessing the target model, we use a surrogate model instead. The surrogate model generates adversarial sentences for a target-agnostic attack. During this process, we leverage controlled generation techniques. We evaluate our proposed method on eight datasets. Experimental results demonstrate our method’s effectiveness including high transferability and the high quality of the generated adversarial examples, and prove its practical in hard black-box settings.

Automatic code question answering aims to generate precise answers to questions about code by analyzing code snippets. To provide an appropriate answer, it is necessary to accurately understand the relevant part of the code and correctly interpret the intent of the question. However, in real-world scenarios, the questioner often provides only a portion of the code along with the question, making it challenging to find an answer. The responder should be capable of providing a suitable answer using such limited information. We propose a knowledge-based framework, CoRAC, an automatic code question responder that enhances understanding through selective API document retrieval and question semantic intent clustering. We evaluate our method on three real-world benchmark datasets and demonstrate its effectiveness through various experiments. We also show that our method can generate high-quality answers compared to large language models, such as ChatGPT.

Automatic processing of source code, such as code clone detection and software vulnerability detection, is very helpful to software engineers. Large pre-trained Programming Language (PL) models (such as CodeBERT, GraphCodeBERT, CodeT5, etc.), show very powerful performance on these tasks. However, these PL models are vulnerable to adversarial examples that are generated with slight perturbation. Unlike natural language, an adversarial example of code must be semantic-preserving and compilable. Due to the requirements, it is hard to directly apply the existing attack methods for natural language models. In this paper, we propose DIP (Dead code Insertion based Black-box Attack for Programming Language Model), a high-performance and effective black-box attack method to generate adversarial examples using dead code insertion. We evaluate our proposed method on 9 victim downstream-task large code models. Our method outperforms the state-of-the-art black-box attack in both attack efficiency and attack quality, while generated adversarial examples are compiled preserving semantic functionality.