Table question answering (TQA) requires accurate retrieval and reasoning over tabular data.Existing approaches attempt to retrieve queryrelevant content before leveraging large language models (LLMs) to reason over long tables.However, these methods often fail to accurately retrieve contextually relevant data which results in information loss, and suffer from excessive encoding overhead.In this paper, we propose TALON, a multi-agent framework designed for question answering over long tables.TALON features a planning agent that iteratively invokes a tool agent to access and manipulate tabular data based on intermediate feedback, which progressively collects necessary information for answer generation, while a critic agent ensures accuracy and efficiency in tool usage and planning.In order to comprehensively assess the effectiveness of TALON, we introduce two benchmarks derived from the WikiTableQuestion and BIRD-SQL datasets, which contain tables ranging from 50 to over 10,000 rows.Experiments demonstrate that TALON achieves average accuracy improvements of 7.5% and 12.0% across all language models, establishing a new state-of-the-art in long-table question answering.Our code is publicly available

Although the rise of large language models (LLMs) has introduced new opportunities for time series forecasting, existing LLM-based solutions require excessive training and exhibit limited transferability. In view of these challenges, we propose TimeRAG, a framework that incorporates Retrieval-Augmented Generation (RAG) into time series forecasting LLMs, which constructs a time series knowledge base from historical sequences, retrieves reference sequences from the knowledge base that exhibit similar patterns to the query sequence measured by Dynamic Time Warping (DTW), and combines these reference sequences and the prediction query as a textual prompt to the time series forecasting LLM. Experiments on datasets from various domains show that the integration of RAG improved the prediction accuracy of the original model by 2.97% on average.

Retrieval-augmented Large Models (RALMs) have emerged as a promising paradigm to enhance large language models (LLMs) by integrating external knowledge. However, the inherent complexity of vector-based retrieval often introduces noise and inaccuracies that can compromise the model’s performance. While existing approaches primarily focus on filtering retrieved contexts, our study reveals a novel perspective: seemingly irrelevant or contradictory knowledge can serve as valuable learning signals for LLMs to improve the data quality of vector database. We propose the NDIC (Negatives Driven Index Correction) framework, a context-aware retrieval method inspired by relational database theory. By introducing Contexts Clear Matching Dependence (CCMDs), our approach enables LLM to precisely categorize retrieval contexts into three distinct types: positively matched, negatively matched, and unclear. Unlike traditional methods, we strategically utilize both positively and negatively matched contexts to refine the learning process and enhance knowledge utilization. Experiments across diverse LLMs demonstrate a 6.17% improvement in accuracy in challenging question-answering tasks, effectively transforming potentially noisy retrievals into structured learning opportunities.

Shilong Pan, Zhiliang Tian, Liang Ding, Haoqi Zheng, Zhen Huang, Zhihua Wen, Dongsheng Li. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Text classification tasks often encounter fewshot scenarios with limited labeled data, and addressing data scarcity is crucial.Data augmentation with mixup merges sample pairs to generate new pseudos, which can relieve the data deficiency issue in text classification.However, the quality of pseudo-samples generated by mixup exhibits significant variations.Most of the mixup methods fail to consider the varying degree of learning difficulty in different stages of training.And mixup generates new samples with one-hot labels, which encourages the model to produce a high prediction score for the correct class that is much larger than other classes, resulting in the model's over-confidence.In this paper, we propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification, which can generate more adaptive and model-friendly pseudo samples for the model training.SE caters to the growth of the model learning ability and adapts to the ability when generating training samples.To alleviate the model over-confidence, we introduce an instance-specific label smoothing regularization approach, which linearly interpolates the model's output and one-hot labels of the original samples to generate new soft labels for label mixing up.Through experimental analysis, experiments show that our SE brings consistent and significant improvements upon different mixup methods.In-depth analyses demonstrate that SE enhances the model's generalization ability.