The Myers-Briggs Type Indicator (MBTI) is one of the most influential personality theories reflecting individual differences in thinking, feeling, and behaving. MBTI personality detection has garnered considerable research interest and has evolved significantly over the years. However, this task tends to be overly optimistic, as it currently does not align well with the natural distribution of population personality traits. Specifically, the self-reported labels in existing datasets result in data quality issues and the hard labels fail to capture the full range of population personality distributions. In this paper, we identify the task by constructing MBTIBench, the first manually annotated MBTI personality detection dataset with soft labels, under the guidance of psychologists. Our experimental results confirm that soft labels can provide more benefits to other psychological tasks than hard labels. We highlight the polarized predictions and biases in LLMs as key directions for future research.

The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive databases and generate SQL. However, the performance of current methods is constrained by single-hop retrieval, and existing multi-hop retrieval of open-domain question answering is not directly applicable due to the tendency to retrieve tables similar to the retrieved ones but irrelevant to the question. Since the questions in text-to-SQL usually contain all required information, while previous multi-hop retrieval supplements the questions with retrieved documents. Therefore, we propose the multi-hop table retrieval with removal (MURRE), which removes previously retrieved information from the question to guide the retriever towards unretrieved relevant tables. Our experiments on two open-domain text-to-SQL datasets demonstrate an average improvement of 5.7% over the previous state-of-the-art results.

The table reasoning task, crucial for efficient data acquisition, aims to answer questions based on the given table .Recently, reasoning large language models (RLLMs) with Long Chain-of-Thought (Long CoT) significantly enhance reasoning capabilities, leading to brilliant performance on table reasoning.However, Long CoT suffers from high cost for training and exhibits low reliability due to table content hallucinations.Therefore, we propose Rowof-Thought (ROT), which performs iteratively row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal.Scaling reasoning length by rowwise traversal and leveraging reflection capabilities of LLMs, ROT is training-free.The sequential traversal encourages greater attention to the table, thus reducing hallucinations.Experiments show that ROT, using non-reasoning models, outperforms RLLMs by an average of 4.3%, and achieves state-of-the-art results on WikiTableQuestions and TableBench with comparable models, proving its effectiveness.Also, ROT outperforms Long CoT with fewer reasoning tokens, indicating higher efficiency.

Numerical reasoning is a vital capability for natural language processing models to understand and process numerical information in real-world scenarios. Most current methods first generate the Intermediate Meaning Representations (IMRs) of questions and then generate answers. Current SOTA methods generate programs as IMRs with large language models (LLMs). Intuitively, equations have fewer restrictions and closer semantics to the question than programs, leading to higher generation accuracy. However, current LLMs generate equations worse than programs, where we assume that the equation data is rare in pre-training data compared to programs. So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems: (1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs; (2) Empirically, how to improve the generation accuracy of equations with LLMs. For the first problem, we propose and prove a proposition to theoretically compare the generation accuracy of different IMRs. For the second problem, we present a method called Boosting Numerical ReasonIng by Decomposing the Generation of Equations Bridge, which can improve the accuracy of LLMs in generating equations as IMRs by reducing the tendency of generating constant expressions and programs. Our method improves the performance by 2.2%, 0.9%, and 1.7% on GSM8K, SVAMP, and Algebra datasets compared to the previous state-of-the-art methods under the single reasoning path setting. Our code and prompts are available at https://github.com/zirui-HIT/Bridge_for_Numerical_Reasoning}.

Numerical reasoning is an essential ability for NLP systems to handle numeric information.Recent research indicates that fine-tuning a small-scale model to learn generating reasoning processes alongside answers can significantly enhance performance.However, current methods have the limitation that most methods generate reasoning processes with large language models (LLMs), which are "unreliable" since such processes could contain information unrelated to the answer.To address this limitation, we introduce Enhancing NumeriCal reasOning with Reliable procEsses (ENCORE), which derives the reliable reasoning process by decomposing the answer formula, ensuring which fully supports the answer.Nevertheless, models could lack enough data to learn the reasoning process generation adequately, since our method generates only one single reasoning process for one formula.To overcome this difficulty, we present a series of pre-training tasks to help models learn the reasoning process generation with synthesized data.The experiments show that ENCORE yields improvement on all five experimental datasets with an average of 1.8%, proving the effectiveness of our method 1 .* Corresponding author. 1 Our code is released in link. 2 For the sake of conciseness in this paper, we collectively refer to these elements as formulas.

Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.