Task-specific data selection, which aims to identify the most relevant training instances from a large corpus to optimize performance on a target task, is a critical challenge in modern AI. Prevailing methods typically rely on either representation clustering or gradient-based influence estimation. However, these approaches have notable limitations. Representation-based methods rely on static features; they measure semantic proximity but are agnostic to the process of learning. Conversely, influence-based methods, while capturing optimization directions, often focus narrowly on aligning with the validation loss, which may not fully correlate with the desired capabilities. To address these issues, we propose TRACE, a novel algorithm that simultaneously considers data consistency in the optimization direction and representation space, and performs TRajectory-based Activation Change Estimation to select instruction. Specifically, TRACE first performs a targeted weight update using the validation set. It then captures the optimization trajectory by calculating the change in neuron activations for each before and after this update. By selecting data whose activation change are most similar to those of the validation set, TRACE ensures alignment in both the representational and optimization domains. Our experiments demonstrate that TRACE outperforms baseline methods across various tasks, particularly in complex, data-scarce scenarios.

Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, Maosong Sun. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks.However, interpreting charts with textual descriptions often leads to information loss, as it fails to fully capture the dense information embedded in charts.In contrast, parsing charts into code provides lossless representations that can effectively contain all critical details.Although existing open-source MLLMs have achieved success in chart understanding tasks, they still face two major challenges when applied to chart-to-code tasks: (1) Low executability and poor restoration of chart details in the generated code and (2) Lack of large-scale and diverse training data.To address these challenges, we propose ChartCoder, the first dedicated chart-to-code MLLM, which leverages Code LLMs as the language backbone to enhance the executability of the generated code.Furthermore, we introduce Chart2Code-160k, the first large-scale and diverse dataset for chartto-code generation, and propose the Snippetof-Thought (SoT) method, which transforms direct chart-to-code generation data into stepby-step generation.Experiments demonstrate that ChartCoder, with only 7B parameters, surpasses existing open-source MLLMs on chartto-code benchmarks, achieving superior chart restoration and code excitability.Our code is available at https://github.com/thunlp/ ChartCoder.89 seed code with 27 chart types Available functions and parameters

Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data.Various approaches have been proposed to address these challenges.One approach involves training specialized retrievers to select relevant information, thereby reducing the input length.Another approach is to transform diverse modalities of data into a single modality, simplifying the task difficulty and enabling more straightforward processing.In this paper, we propose HPROPRO, a novel program-based prompting framework for the hybrid question answering task.HPRO-PRO follows the code generation and execution paradigm.In addition, HPROPRO integrates various functions to tackle the hybrid reasoning scenario.Specifically, HPROPRO contains function declaration and function implementation to perform hybrid information-seeking over data from various sources and modalities, which enables reasoning over such data without training specialized retrievers or performing modal transformations.Experimental results on two typical hybrid question answering benchmarks HybridQA and MultiModalQA demonstrate the effectiveness of HPROPRO: it surpasses all baseline systems and achieves the best performances in the few-shot settings on both datasets 1 .

Existing KG-augmented models for commonsense question answering primarily focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs).However, they ignore (i) the effectively fusing and reasoning over question context representations and the KG representations, and (ii) automatically selecting relevant nodes from the noisy KGs during reasoning.In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LM and GNN and the dynamic KGs pruning mechanism.Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions.Then, the dynamic pruning module uses the attention weights generated by joint reasoning to prune irrelevant KG nodes recursively.We evaluate JointLK on the Com-monsenseQA and OpenBookQA datasets, and demonstrate its improvements to the existing LM and LM+KG models, as well as its capability to perform interpretable reasoning 1 .

Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.

Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning.Existing methods lack attention to take advantage of the combination of linguistic information and symbolic information.In this work, we propose HeterTFV, a graph-based reasoning approach, that learns to combine linguistic information and symbolic information effectively.We first construct a program graph to encode programs, a kind of LISP-like logical form, to learn the semantic compositionality of the programs.Then we construct a heterogeneous graph to incorporate both linguistic information and symbolic information by introducing program nodes into the heterogeneous graph.Finally, we propose a graph-based reasoning approach to reason over the multiple types of nodes to make an effective combination of both types of information.Experimental results on a large-scale benchmark dataset TABFACT illustrate the effect of our approach.