31 citations · 115 across the 14 of their papers we have counts for
14 papers
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models
Ansong Ni, Pengcheng Yin, Yilun Zhao +11
Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a…
LEVER: Learning to Verify Language-to-Code Generation with Execution
Ansong Ni, Srini Iyer, Dragomir Radev +4
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation. State-of-the-art approaches in this area combine LLM…
Explicit Knowledge Transfer for Weakly-Supervised Code Generation
Zhangir Azerbayev, Ansong Ni, Hailey Schoelkopf +1
Large language models (LLMs) can acquire strong code-generation capabilities through few-shot learning. In contrast, supervised fine-tuning is still needed for smaller models to ac…
FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao +32
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the…
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
Ansong Ni, Jeevana Priya Inala, Chenglong Wang +4
Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school…
Leveraging Locality in Abstractive Text Summarization
Yixin Liu, Ansong Ni, Linyong Nan +4
Neural attention models have achieved significant improvements on many natural language processing tasks. However, the quadratic memory complexity of the self-attention module with…
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi +20
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.…
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
Yusen Zhang, Ansong Ni, Ziming Mao +6
Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unab…
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Ziming Mao, Chen Henry Wu, Ansong Ni +7
Transformer-based models have achieved state-of-the-art performance on short-input summarization. However, they still struggle with summarizing longer text. In this paper, we prese…
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Yusen Zhang, Ansong Ni, Tao Yu +6
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…
SummerTime: Text Summarization Toolkit for Non-experts
Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma +5
Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summ…
Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization
Ansong Ni, Matt Gardner, Pradeep Dasigi
Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is t…
SOAR: A Synthesis Approach for Data Science API Refactoring
Ansong Ni, Daniel Ramos, Aidan Yang +4
With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To matc…
Merging Weak and Active Supervision for Semantic Parsing
Ansong Ni, Pengcheng Yin, Graham Neubig
A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-de…