QA-TOOLBOX: Conversational Question-Answering for process task guidance in manufacturing

• URL: http://arxiv.org/abs/2412.02638v1
• Date: Tue, 03 Dec 2024 18:10:31 GMT
• Title: QA-TOOLBOX: Conversational Question-Answering for process task guidance in manufacturing
• Authors: Ramesh Manuvinakurike, Elizabeth Watkins, Celal Savur, Anthony Rhodes, Sovan Biswas, Gesem Gudino Mejia, Richard Beckwith, Saurav Sahay, Giuseppe Raffa, Lama Nachman,
• Abstract summary: This dataset consists of representative samples of interactions with technicians working in an advanced manufacturing setting. The dataset consists of 200,000+ question/answer pairs that refer to the spec document and are grounded in narrations and/or video demonstrations.
• Score: 6.377282332225302
• License:
• Abstract: In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system. The dataset consists of representative samples of interactions with technicians working in an advanced manufacturing setting. The purpose of this work to explore the task, data augmentation for the supported tasks and evaluating the performance of the existing LLMs. We observe that that task is complex requiring understanding from procedure specification documents, actions and objects sequenced temporally. The dataset consists of 200,000+ question/answer pairs that refer to the spec document and are grounded in narrations and/or video demonstrations. We compared the performance of several popular open-sourced LLMs by developing a baseline using each LLM and then compared the responses in a reference-free setting using LLM-as-a-judge and compared the ratings with crowd-workers whilst validating the ratings with experts.

Related papers

• Dynamic Ensemble Reasoning for LLM Experts [35.774197263383996]
  We propose a Dynamic Ensemble Reasoning paradigm, called DER, to integrate the strengths of multiple LLM experts conditioned on dynamic inputs. Our method uses fewer computational resources to achieve better performance compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-12-10T12:05:56Z)
• Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models [22.676688441884465]
  Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations.
  arXiv  Detail & Related papers  (2024-10-25T23:38:28Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems [99.17123445211115]
  We introduce DocBench, a benchmark to evaluate large language model (LLM)-based document reading systems. Our benchmark involves the recruitment of human annotators and the generation of synthetic questions. It includes 229 real documents and 1,102 questions, spanning across five different domains and four major types of questions.
  arXiv  Detail & Related papers  (2024-07-15T13:17:42Z)
• A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation [15.153844486572932]
  This paper proposes a practice-friendly LLM-enhanced paradigm with preference parsing (P2Rec) for sequential recommender systems (SRS) Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model. Our goal is to let LLM learn to reconstruct a corresponding prior preference distribution from each user's interaction sequence.
  arXiv  Detail & Related papers  (2024-06-01T07:18:56Z)
• RepEval: Effective Text Evaluation with LLM Representation [55.26340302485898]
  RepEval is a metric that leverages the projection of Large Language Models (LLMs) representations for evaluation. Our work underscores the richness of information regarding text quality embedded within LLM representations, offering insights for the development of new metrics.
  arXiv  Detail & Related papers  (2024-04-30T13:50:55Z)
• Benchmarking LLMs on the Semantic Overlap Summarization Task [9.656095701778975]
  This paper comprehensively evaluates Large Language Models (LLMs) on the Semantic Overlap Summarization (SOS) task. We report well-established metrics like ROUGE, BERTscore, and SEM-F1$ on two different datasets of alternative narratives.
  arXiv  Detail & Related papers  (2024-02-26T20:33:50Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance [60.40541387785977]
  Small foundational models can display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data. In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following. Experimental results show that fine-tuning LLaMA on writing instruction data significantly improves its ability on writing tasks.
  arXiv  Detail & Related papers  (2023-05-22T16:56:44Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.