Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. Integrating computational tools with LRMs remains challenging, particularly in activating and enhancing models' tool-use capabilities without compromising their reasoning strengths. We address these challenges through START (Self-taught Reasoner with Tools), introducing two key innovations: (1) Hint-infer, a training-free approach that activates LRMs' latent tool-use capabilities through artificial hints, enabling test-time performance scaling; (2) Hint-RFT, a self-training framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). Our analysis reveals that START not only enhances accuracy but also improves reasoning efficiency through strategic tool utilization, demonstrating broad applicability in complex reasoning scenarios.

START: Self-taught Reasoner with Tools

Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-ranging code-related tasks. However, most previous existing methods mainly view each programming language in isolation and ignore the knowledge transfer among different programming languages. To bridge the gap among different programming languages, we introduce a novel multi-agent collaboration framework to enhance multilingual instruction tuning for code LLMs, where multiple language-specific intelligent agent components with generation memory work together to transfer knowledge from one language to another efficiently and effectively. Specifically, we first generate the language-specific instruction data from the code snippets and then provide the generated data as the seed data for language-specific agents. Multiple language-specific agents discuss and collaborate to formulate a new instruction and its corresponding solution (A new programming language or existing programming language), To further encourage the cross-lingual transfer, each agent stores its generation history as memory and then summarizes its merits and faults. Finally, the high-quality multilingual instruction data is used to encourage knowledge transfer among different programming languages to train Qwen2.5-xCoder. Experimental results on multilingual programming benchmarks demonstrate the superior performance of Qwen2.5-xCoder in sharing common knowledge, highlighting its potential to reduce the cross-lingual gap.

Multi-Agent Collaboration for Multilingual Code Instruction Tuning

Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce Nuggets, a novel and efficient methodology that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. Nuggets assesses the potential of individual instruction examples to act as effective one-shot learning instances, thereby identifying those that can significantly improve performance across diverse tasks. Nuggets utilizes a scoring system based on the impact of candidate examples on the perplexity of a diverse anchor set, facilitating the selection of the most advantageous data for instruction tuning. Through rigorous evaluations on two benchmarks, namely MT-Bench and Alpaca-Eval, our study illustrates that instruction tuning with the top 1\% of examples curated by Nuggets substantially outperforms conventional methods employing the entire dataset.

One-Shot Learning as Instruction Data Prospector for Large Language Models

The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to-SQL tasks. In this paper, we introduce a synthetic data approach that combines data produced by larger, more powerful models (strong models) with error information data generated by smaller, not well-aligned models (weak models). The method not only enhances the domain generalization of text-to-SQL models but also explores the potential of error data supervision through preference learning. Furthermore, we employ the synthetic data approach for instruction tuning on open-source LLMs, resulting SENSE, a specialized text-to-SQL model. The effectiveness of SENSE is demonstrated through state-of-the-art results on the SPIDER and BIRD benchmarks, bridging the performance gap between open-source models and methods prompted by closed-source models.

Synthesizing Text-to-SQL Data from Weak and Strong LLMs

Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, with the expectation that a single iteration of demonstrations processing can generalize effectively to a given test sample. However, this perspective overlooks the potential benefits derived from multiple iterations involving demonstrations, a practice aligning more closely with the iterative decision-making process exhibited by humans, who often learn through analogy. In this study, we introduce a novel two-stage framework to boost ICL in LLMs. Specifically, our framework delineates the ICL process into two distinct stages: Deep-Thinking and test stages. The Deep-Thinking stage incorporates a unique attention mechanism, i.e., iterative enhanced attention, which enables multiple rounds of information accumulation. This mechanism operates by manipulating the Key-Value matrices without training, fostering enhanced understanding capabilities in LLMs by thinking demonstrations multiple times. We evaluated Deep-Thinking across a range of benchmarks and LLMs, showing its superior performance over vanilla ICL methods and its effectiveness in challenging tasks where demonstration selection is infeasible.

Iterative Forward Tuning Boosts In-Context Learning in Language Models

The technology of text-to-SQL has significantly enhanced the efficiency of accessing and manipulating databases.
However, limited research has been conducted to study its vulnerabilities emerging from malicious user interaction. 
By proposing TrojanSQL, a backdoor-based SQL injection framework for text-to-SQL systems,
we show how state-of-the-art text-to-SQL parsers can be easily misled to produce harmful SQL statements
that can invalidate user queries or compromise sensitive information about the database.
The study explores two specific injection attacks, namely $\textit{boolean-based injection}$ and $\textit{union-based injection}$,
which use different types of triggers to achieve distinct goals in compromising the parser.
Experimental results demonstrate that both medium-sized models based on fine-tuning and
LLM-based parsers using prompting techniques are vulnerable to this type of attack,
with attack success rates as high as 99\% and 89\%, respectively.
We hope that this study will raise more concerns about the potential security risks of building natural language interfaces to databases.

TrojanSQL: SQL Injection against Natural Language Interface to Database

Large Language Models (LLMs) have achieved remarkable success in many formal language oriented tasks, such as structural data-to-text and semantic parsing.
However current benchmarks mostly follow the data distribution of the pre-training data of LLMs.
Therefore, a natural question rises that do LLMs really understand the structured semantics of formal languages.
In this paper, we investigate this problem on a special case, converse binary relation.
We introduce a new benchmark ConvRe focusing on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets.
Our ConvRE features two tasks, Re2Text and Text2Re, which are formulated as multi-choice question answering to evaluate LLMs' ability to determine the matching between relations and associated text.
For the evaluation protocol, apart from different prompting methods, we further introduce variants to the test text and few-shot example text. 
We conduct experiments on three popular LLM families and have observed various scaling trends. 
The results suggest that LLMs
often resort to shortcut learning and still face challenges on our proposed benchmark.

An Investigation of LLMs’ Inefficacy in Understanding Converse Relations

Context information modeling is an important task in conversational KBQA. However, existing methods usually assume the independence of utterances and model them in isolation. In this paper, we propose a History Semantic Graph Enhanced KBQA model (HSGE) that is able to effectively model long-range semantic dependencies in conversation history while maintaining low computational cost. The framework incorporates a context-aware encoder, which employs a dynamic memory decay mechanism and models context at different levels of granularity. We evaluate HSGE on a widely used benchmark dataset for complex sequential question answering. Experimental results demonstrate that it outperforms existing baselines averaged on all question types.

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History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling

Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality. In this paper, we propose a Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph (SPRING) with abilities of reasoning multi-hops spatial relations and connecting them with visual attributes in crowded situated scenarios. Specifically, we design two types of Multimodal Question Answering (MQA) tasks to pretrain the agent. All QA pairs utilized during pretraining are generated from novel Increment Layout Graphs (ILG). QA pair difficulty labels automatically annotated by ILG are used to promote MQA-based Curriculum Learning. Experimental results verify the SPRING's effectiveness, showing that it significantly outperforms state-of-the-art approaches on both SIMMC 1.0 and SIMMC 2.0 datasets. We release our code and data at https://github.com/LYX0501/SPRING.

Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph

The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years, as it can assist end users in efficiently extracting vital information from databases without the need for technical background.  One of the major challenges in text-to-SQL parsing is domain generalization, i.e., how to generalize well to unseen databases.  Recently, the pretrained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization. In this work, we explore ways to further augment the pretrained T5 model with specialized components for text-to-SQL parsing. Such components are expected to introduce structural inductive biases into text-to-SQL parsers thus improving model's capacity on (potentially multi-hop) reasoning, which is critical for generating structure-rich SQLs.  To this end, we propose a new architecture Graphix-T5, a mixed model with the standard pre-trained transformer model augmented by some specially-designed graph-aware layers. Extensive experiments and analysis demonstrate the effectiveness of Graphix-T5 across four text-to-SQL benchmarks: Spider, SYN, Realistic and DK. Graphix-T5 surpass all other T5-based parsers with a significant margin, achieving new state-of-the-art performance.  Notably, Graphix-T5-large reach performance superior to the original T5-large by 5.7% on Exact Match (EM) accuracy and 6.6% on Execution accuracy (EX). This even outperforms the T5-3B by 1.2% on EM and 1.5% on EX. 

Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing

Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain context-dependent semantic parsing has become a new focus of research. Central to the problem is the challenge of leveraging contextual information of both natural language queries and database schemas in the interaction history.  In this paper, we present a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. The framework employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation, which is further enhanced with a powerful reranking model. At the time of writing, we demonstrate that the proposed framework outperforms all existing models by large margins, achieving new state-of-the-art performance on two large-scale benchmarks, the SParC and CoSQL datasets. Specifically, the model attains a 55.8% question-match and 30.8% interaction-match accuracy on SParC, and a 46.8% question-match and 17.0% interaction-match accuracy on CoSQL.

Binyuan Hui

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