ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models

The task of Text-to-SQL enables anyone to retrieve information from SQL databases using natural language. Despite several challenges, recent models have made remarkable advancements in this task using large language models (LLMs). Interestingly, we find that LLM-based models without fine-tuning exhibit distinct natures compared to their fine-tuned counterparts, leading to inadequacies in current evaluation metrics to accurately convey their performance. Thus, we analyze the two primary metrics, Test Suite Execution Accuracy (EXE) and Exact Set Matching Accuracy (ESM), to examine their robustness for this task and address shortcomings. We compare the performance of 9 LLM-based models using EXE, the original ESM, and our improved ESM (called ESM+). Our results show that EXE and ESM have high false positive and negative rates of 11.3% and 13.9%, while ESM+ gives those of 0.1% and 2.6% respectively, providing a significantly more stable evaluation. We release the ESM+ script as open-source for the community to contribute, while enjoying a more reliable assessment of Text-to-SQL.

Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models

Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users. Commonsense-augmented dialogue models have been proposed that aim to infer commonsense knowledge from dialogue contexts in order to improve response quality. However, existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsense inferences during response generation. In this study, we explore the impact of explicit reasoning against implicit reasoning over commonsense for dialogue response generation. Our findings demonstrate that separating commonsense reasoning into explicit steps for generating, selecting, and integrating commonsense into responses leads to better dialogue interactions, improving naturalness, engagement, specificity, and overall quality. Subsequent analyses of these findings unveil insights into the effectiveness of various types of commonsense in generating responses and the particular response traits enhanced through explicit reasoning for commonsense integration. Our work advances research in open-domain dialogue by achieving a new state-of-the-art in commonsense-augmented response generation.

Leveraging Diverse Data Generation for Adaptable Zero-Shot Dialogue State Tracking

This work demonstrates that substantial gains in zero-shot dialogue state tracking (DST) accuracy can be achieved by increasing the diversity of training data using synthetic data generation techniques. Current DST training resources are severely limited in the number of application domains and slot types they cover due to the high costs of data collection, resulting in limited adaptability to new domains. The presented work overcomes this challenge using a novel, fully automatic data generation approach to create synthetic zero-shot DST training resources. Unlike previous approaches for generating DST data, the presented approach generates entirely new application domains to generate dialogues, complete with silver dialogue state annotations and slot descriptions. This approach is used to create the D0T dataset for training zero-shot DST models, which covers an unprecedented 1,000+ domains. Experiments performed on the MultiWOZ benchmark indicate that training models on diverse synthetic data yields a performance improvement of +6.7% Joint Goal Accuracy, achieving results competitive with much larger models.

Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large Language Models for Trauma Assessments

The shortage of clinical workforce presents significant challenges in mental healthcare, limiting access to formal diagnostics and services. We aim to tackle this shortage by integrating a customized large language model (LLM) into the workflow, thus promoting equity in mental healthcare for the general population. Although LLMs have showcased their capability in clinical decision-making, their adaptation to severe conditions like Post-traumatic Stress Disorder (PTSD) remains largely unexplored. Therefore, we collect 411 clinician-administered diagnostic interviews and devise a novel approach to obtain high-quality data. Moreover, we build a comprehensive framework to automate PTSD diagnostic assessments based on interview contents by leveraging two state-of-the-art LLMs, GPT-4 and Llama-2, with potential for broader clinical diagnoses. Our results illustrate strong promise for LLMs, tested on our dataset, to aid clinicians in diagnostic validation. To the best of our knowledge, this is the first AI system that fully automates assessments for mental illness based on clinician-administered interviews.

What is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models

Bias is a disproportionate prejudice in favor of one side against another. Due to the success of transformer-based Masked Language Models (MLMs) and their impact on many NLP tasks, a systematic evaluation of bias in these models is needed more than ever. While many studies have evaluated gender bias in English MLMs, only a few works have been conducted for the task in other languages. This paper proposes a multilingual approach to estimate gender bias in MLMs from 5 languages: Chinese, English, German, Portuguese, and Spanish. Unlike previous work, our approach does not depend on parallel corpora coupled with English to detect gender bias in other languages using multilingual lexicons. Moreover, a novel model-based method is presented to generate sentence pairs for a more robust analysis of gender bias, compared to the traditional lexicon-based method. For each language, both the lexicon-based and model-based methods are applied to create two datasets respectively, which are used to evaluate gender bias in an MLM specifically trained for that language using one existing and 3 new scoring metrics. Our results show that the previous approach is data-sensitive and not stable as it does not remove contextual dependencies irrelevant to gender. In fact, the results often flip when different scoring metrics are used on the same dataset, suggesting that gender bias should be studied on a large dataset using multiple evaluation metrics for best practice.

Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model

Factual inconsistency poses a significant hurdle for the commercial deployment of abstractive summarizers. Under this Large Language Model (LLM) era, this work focuses around two important questions: what is the best way to leverage LLM for factual inconsistency detection, and how could we distill a smaller LLM with both high efficiency and efficacy? Three zero-shot paradigms are firstly proposed and evaluated across five diverse datasets: direct inference on the entire summary or each summary window; entity verification through question generation and answering. Experiments suggest that LLM itself is capable to resolve this task train-free under the proper paradigm design, surpassing strong trained baselines by 2.8% on average. To further promote practical utility, we then propose training strategies aimed at distilling smaller open-source LLM that learns to score the entire summary at once with high accuracy, which outperforms the zero-shot approaches by much larger LLM, serving as an effective and efficient ready-to-use scorer.

ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI

Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing datasets tend to lack in-depth details, restate information already present in the conversation, and often fail to capture the multifaceted nature of commonsense reasoning. In response to these limitations, we compile a new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, ConvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences. Our dataset contains over 500,000 inferences across 12,000 dialogues with 10 popular inference types, which empowers the training of generative commonsense models for dialogue that are superior in producing plausible inferences with high novelty when compared to models trained on the previous datasets. To the best of our knowledge, ConvoSense is the first of its kind to provide such a multitude of novel inferences at such a large scale.

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones. We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing. Our IRB-approved evaluation of the prediction of self-reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AUROC improvement and 8.21% MAE reduction.

Aligning Speakers: Evaluating and Visualizing Text-based Diarization Using Efficient Multiple Sequence Alignment (Extended Version)

This paper presents a novel evaluation approach to text-based speaker diarization (SD), tackling the limitations of traditional metrics that do not account for any contextual information in text. Two new metrics are proposed, Text-based Diarization Error Rate and Diarization F1, which perform utterance- and word-level evaluations by aligning tokens in reference and hypothesis transcripts. Our metrics encompass more types of errors compared to existing ones, allowing us to make a more comprehensive analysis in SD. To align tokens, a multiple sequence alignment algorithm is introduced that supports multiple sequences in the reference while handling high-dimensional alignment to the hypothesis using dynamic programming. Our work is packaged into two tools, align4d providing an API for our alignment algorithm and TranscribeView for visualizing and evaluating SD errors, which can greatly aid in the creation of high-quality data, fostering the advancement of dialogue systems.

Exploring the Impact of Human Evaluator Group on Chat-Oriented Dialogue Evaluation

Human evaluation has been widely accepted as the standard for evaluating chat-oriented dialogue systems. However, there is a significant variation in previous work regarding who gets recruited as evaluators. Evaluator groups such as domain experts, university students, and professional annotators have been used to assess and compare dialogue systems, although it is unclear to what extent the choice of an evaluator group can affect results. This paper analyzes the evaluator group impact on dialogue system evaluation by testing 4 state-of-the-art dialogue systems using 4 distinct evaluator groups. Our analysis reveals a robustness towards evaluator groups for Likert evaluations that is not seen for Pairwise, with only minor differences observed when changing evaluator groups. Furthermore, two notable limitations to this robustness are observed, which reveal discrepancies between evaluators with different levels of chatbot expertise and indicate that evaluator objectivity is beneficial for certain dialogue metrics.