230 citations · 564 across the 27 of their papers we have counts for
31 papers
Direct-Inverse Prompting: Analyzing LLMs' Discriminative Capacity in Self-Improving Generation
Jihyun Janice Ahn, Ryo Kamoi, Lu Cheng +2
Mainstream LLM research has primarily focused on enhancing their generative capabilities. However, even the most advanced LLMs experience uncertainty in their outputs, often produc…
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Ryo Kamoi, Yusen Zhang, Nan Zhang +2
Self-correction is an approach to improving responses from large language models (LLMs) by refining the responses using LLMs during inference. Prior work has proposed various self-…
Pruning as a Domain-specific LLM Extractor
Nan Zhang, Yanchi Liu, Xujiang Zhao +5
Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment cos…
Evaluating LLMs at Detecting Errors in LLM Responses
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou +12
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted o…
Large Language Models for Mathematical Reasoning: Progresses and Challenges
Janice Ahn, Rishu Verma, Renze Lou +3
Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the de…
MT-Ranker: Reference-free machine translation evaluation by inter-system ranking
Ibraheem Muhammad Moosa, Rui Zhang, Wenpeng Yin
Traditionally, Machine Translation (MT) Evaluation has been treated as a regression problem -- producing an absolute translation-quality score. This approach has two limitations: i…
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
Nan Zhang, Yusen Zhang, Wu Guo +2
Summaries of medical text shall be faithful by being consistent and factual with source inputs, which is an important but understudied topic for safety and efficiency in healthcare…
DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
Yilun Zhao, Yitao Long, Hongjun Liu +7
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-wo…
FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains
Yilun Zhao, Hongjun Liu, Yitao Long +3
We introduce FinanceMath, a novel benchmark designed to evaluate LLMs' capabilities in solving knowledge-intensive math reasoning problems. Compared to prior works, this study feat…
Fair Abstractive Summarization of Diverse Perspectives
Yusen Zhang, Nan Zhang, Yixin Liu +9
People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and pol…
Logic-Consistency Text Generation from Semantic Parses
Chang Shu, Yusen Zhang, Xiangyu Dong +3
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries. This is challenging due to two reason…
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Anup Sarma, Sonali Singh, Huaipan Jiang +3
Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning…
Cross-language Sentence Selection via Data Augmentation and Rationale Training
Yanda Chen, Chris Kedzie, Suraj Nair +4
This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentenc…
FeTaQA: Free-form Table Question Answering
Linyong Nan, Chiachun Hsieh, Ziming Mao +14
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to in…
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Yusen Zhang, Xiangyu Dong, Shuaichen Chang +3
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any inpu…
DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan, Dragomir Radev, Rui Zhang +21
We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when…
ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan +7
Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for comm…
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
Michihiro Yasunaga, Jungo Kasai, Rui Zhang +4
Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community. Thi…
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Tao Yu, Rui Zhang, He Yang Er +21
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained f…
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
Rui Zhang, Tao Yu, He Yang Er +7
We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and…
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Rui Zhang, Joel Tetreault
Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email's content. In this paper, we propose and study the…
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
Rui Zhang, Caitlin Westerfield, Sungrok Shim +5
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in b…
SParC: Cross-Domain Semantic Parsing in Context
Tao Yu, Rui Zhang, Michihiro Yasunaga +16
We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It…
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
Tao Yu, Michihiro Yasunaga, Kai Yang +4
Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this pa…