AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models

• URL: http://arxiv.org/abs/2403.15157v2
• Date: Wed, 3 Apr 2024 09:46:51 GMT
• Title: AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models
• Authors: Chaoyun Zhang, Zicheng Ma, Yuhao Wu, Shilin He, Si Qin, Minghua Ma, Xiaoting Qin, Yu Kang, Yuyi Liang, Xiaoyu Gou, Yajie Xue, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang,
• Abstract summary: Allhands is an innovative analytic framework designed for large-scale feedback analysis through a natural language interface. LLMs are large language models that enhance accuracy, robustness, generalization, and user-friendliness. Allhands delivers comprehensive multi-modal responses, including text, code, tables, and images.
• Score: 34.82568259708465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-scale feedback analysis through a natural language interface, leveraging large language models (LLMs). Allhands adheres to a conventional feedback analytic workflow, initially conducting classification and topic modeling on the feedback to convert them into a structurally augmented format, incorporating LLMs to enhance accuracy, robustness, generalization, and user-friendliness. Subsequently, an LLM agent is employed to interpret users' diverse questions in natural language on feedback, translating them into Python code for execution, and delivering comprehensive multi-modal responses, including text, code, tables, and images. We evaluate Allhands across three diverse feedback datasets. The experiments demonstrate that Allhands achieves superior efficacy at all stages of analysis, including classification and topic modeling, eventually providing users with an "ask me anything" experience with comprehensive, correct and human-readable response. To the best of our knowledge, Allhands stands as the first comprehensive feedback analysis framework that supports diverse and customized requirements for insight extraction through a natural language interface.

Related papers

• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• Adapting Large Language Models to Domains via Reading Comprehension [86.24451681746676]
  We explore how continued pre-training on domain-specific corpora influences large language models. We show that training on the raw corpora endows the model with domain knowledge, but drastically hurts its ability for question answering. We propose a simple method for transforming raw corpora into reading comprehension texts.
  arXiv  Detail & Related papers  (2023-09-18T07:17:52Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization [5.4138734778206]
  The rapid growth of information on the Internet has led to an overwhelming amount of opinions and comments on various activities, products, and services. This makes it difficult and time-consuming for users to process all the available information when making decisions. We propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews.
  arXiv  Detail & Related papers  (2023-05-26T03:44:35Z)
• Design Challenges for a Multi-Perspective Search Engine [44.48345943046946]
  We study a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges in order to achieve the goal. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm.
  arXiv  Detail & Related papers  (2021-12-15T18:59:57Z)
• TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing [73.16475763422446]
  We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
  arXiv  Detail & Related papers  (2021-03-21T17:20:38Z)
• Abstractive Summarization of Spoken and Written Instructions with BERT [66.14755043607776]
  We present the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.
  arXiv  Detail & Related papers  (2020-08-21T20:59:34Z)
• Quda: Natural Language Queries for Visual Data Analytics [33.983060903399554]
  We present a new dataset, called Quda, that aims to help V-NLIs recognize analytic tasks from free-form natural language. Our dataset contains $14,035$ diverse user queries, and each is annotated with one or multiple analytic tasks. This work is the first attempt to construct a large-scale corpus for recognizing analytic tasks.
  arXiv  Detail & Related papers  (2020-05-07T05:35:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.